Map generation and control system

ABSTRACT

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

FIELD OF THE DESCRIPTION

The present description relates to agricultural machines, forestrymachines, construction machines and turf management machines.

BACKGROUND

There are a wide variety of different types of agricultural machines.Some agricultural machines include harvesters, such as combineharvesters, sugar cane harvesters, cotton harvesters, self-propelledforage harvesters, and windrowers. Some harvester can also be fittedwith different types of heads to harvest different types of crops.

Agricultural harvesters may operate differently in areas of varyingyield in fields unless the settings in the agricultural harvester arechanged. For instance, when a harvester transitions from an area in afield with a first yield to an area in the field with a second yield,where the second yield is higher than the first yield, the change from areduced amount of grain being harvested to an increased amount of grainmay degrade the performance of the harvester if operating settings arenot changed. Therefore, an operator may attempt to modify control of theharvester upon transitioning between an area of increased or reducedyield during the harvesting operation.

The discussion above is merely provided for general backgroundinformation and is not intended to be used as an aid in determining thescope of the claimed subject matter.

SUMMARY

One or more information maps are obtained by an agricultural workmachine. The one or more information maps map one or more agriculturalcharacteristic values at different geographic locations of a field. Anin-situ sensor on the agricultural work machine senses an agriculturalcharacteristic as the agricultural work machine moves through the field.A predictive map generator generates a predictive map that predicts apredictive agricultural characteristic at different locations in thefield based on a relationship between the values in the one or moreinformation maps and the agricultural characteristic sensed by thein-situ sensor. The predictive map can be output and used in automatedmachine control.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to examples that solveany or all disadvantages noted in the background.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partial pictorial, partial schematic illustration of oneexample of an agricultural harvester.

FIG. 2 is a block diagram showing some portions of an agriculturalharvester in more detail, according to some examples of the presentdisclosure.

FIGS. 3A-3B (collectively referred to herein as FIG. 3) show a flowdiagram illustrating an example of operation of an agriculturalharvester in generating a map.

FIG. 4 is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 5 is a flow diagram showing an example of operation of anagricultural harvester in receiving a vegetative index or historicalyield map, detecting an in-situ yield characteristic, and generating afunctional predictive yield map for presentation or use in controllingthe agricultural harvester during a harvesting operation or both.

FIG. 6A is a block diagram showing one example of a predictive modelgenerator and a predictive map generator.

FIG. 6B is a block diagram showing some examples of in-situ sensors.

FIG. 7 shows a flow diagram illustrating one example of operation of anagricultural harvester involving generating a functional predictive mapusing a prior information map and an in-situ sensor input.

FIG. 8 is a block diagram showing one example of a control zonegenerator.

FIG. 9 is a flow diagram illustrating one example of the operation ofthe control zone generator shown in FIG. 8.

FIG. 10 illustrates a flow diagram showing an example of operation of acontrol system in selecting a target settings value to control anagricultural harvester.

FIG. 11 is a block diagram showing one example of an operator interfacecontroller.

FIG. 12 is a flow diagram illustrating one example of an operatorinterface controller.

FIG. 13 is a pictorial illustration showing one example of an operatorinterface display.

FIG. 14 is a block diagram showing one example of an agriculturalharvester in communication with a remote server environment.

FIGS. 15-17 show examples of mobile devices that can be used in anagricultural harvester.

FIG. 18 is a block diagram showing one example of a computingenvironment that can be used in an agricultural harvester

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the examplesillustrated in the drawings, and specific language will be used todescribe the same. It will nevertheless be understood that no limitationof the scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, methods, and anyfurther application of the principles of the present disclosure arefully contemplated as would normally occur to one skilled in the art towhich the disclosure relates. In particular, it is fully contemplatedthat the features, components, steps, or a combination thereof describedwith respect to one example may be combined with the features,components, steps, or a combination thereof described with respect toother examples of the present disclosure.

The present description relates to using in-situ data taken concurrentlywith an agricultural operation, in combination with prior data, togenerate a functional predictive map and, more particularly, afunctional predictive yield map. In some examples, the functionalpredictive yield map can be used to control an agricultural workmachine, such as an agricultural harvester. As discussed above,performance of an agricultural harvester may be degraded when theagricultural harvester engages areas of varying yield unless machinesettings are also changed. For instance, in an area of reduced yield,the agricultural harvester may move over the ground quickly and movematerial through the machine at an increased feed rate. Whenencountering an area of increased yield, the speed of the agriculturalharvester over the ground may decrease, thereby decreasing the feed rateinto the agricultural harvester, or the agricultural harvester may plug,lose grain, or face other problems. For example, areas of a field havingincreased yield may have crop plants with different physical structuresthan in areas of the field having reduced yield. For instance, in areasof increased yield, some plants may have thicker stalks, broader leaves,larger, or more heads, etc. These variations in plant structure in areasof varying yield may also cause the performance of the agriculturalharvester to vary when the agricultural harvester moves through areas ofvarying yield.

A vegetative index map illustratively maps vegetative index values,which may be indicative of vegetative growth, across differentgeographic locations in a field of interest. One example of a vegetativeindex includes a normalized difference vegetation index (NDVI). Thereare many other vegetative indices, and all of these vegetative indicesare within the scope of the present disclosure. In some examples, avegetative index may be derived from sensor readings of one or morebands of electromagnetic radiation reflected by the plants. Withoutlimitations, these bands may be in the microwave, infrared, visible, orultraviolet portions of the electromagnetic spectrum.

A vegetative index map can thus be used to identify the presence andlocation of vegetation. In some examples, a vegetative index map enablescrops to be identified and georeferenced in the presence of bare soil,crop residue, or other plants, including crop or weeds. For instance,towards the beginning of a growing season, when a crop is in a growingstate, the vegetative index may show the progress of the cropdevelopment. Therefore, if a vegetative index map is generated early inthe growing season or midway through the growing season, the vegetativeindex map may be indicative of the progress of the development of thecrop plants. For instance, the vegetative index map may indicate whetherthe plant is stunted, establishing a sufficient canopy, or other plantattributes that are indicative of plant development.

A historical yield map illustratively maps yield values across differentgeographic locations in one or more field(s) of interest. Thesehistorical yield maps are collected from past harvesting operations onthe field(s). A yield map may show yield in yield value units. Oneexample of a yield value unit includes dry bushels per acre. In someexamples, a historical yield map may be derived from sensor readings ofone or more yield sensors. Without limitation, these yield sensors mayinclude gamma ray attenuation sensors, impact plate sensors, load cells,cameras, or other optical sensors and ultrasonic sensors, among others.

The present discussion also includes predictive maps that predict acharacteristic based on a prior information map and a relationship tosensed data obtained from an in-situ sensor. These predictive mapsinclude a predictive yield map. In one example, the predictive yield mapis generated by receiving a prior vegetative index map and sensed yielddata obtained from an in-situ yield sensor and determining arelationship between the prior vegetative index map and the sensed yielddata obtained from a signal from the in-situ yield sensor, and using therelationship to generate the predictive yield map based on therelationship and the prior vegetative index map. The predictive yieldmap can be created based on other prior information maps or generated inother ways as well. For example, the predictive yield can be generatedbased on satellite images, growth models, weather models, etc. Or forexample, a predictive yield map may be based in whole or in part on atopographic map, a soil type map, a soil constituent map, or a soilhealth map.

The present discussion thus proceeds with respect to examples in which asystem receives one or more of a vegetative index map, a historicalyield map of a field, or a map generated during a prior operation andalso uses an in-situ sensor to detect a characteristic or variableindicative of yield during a harvesting operation. The system generatesa model that models a relationship between the vegetative index valuesor historical yield values from one or more of the maps and the in-situdata from the in-situ sensor. The model is used to generate a functionalpredictive yield map that predicts an anticipated crop yield in thefield. The functional predictive yield map, generated during theharvesting operation, can be presented to an operator or other user orused in automatically controlling an agricultural harvester during theharvesting operation or both.

FIG. 1 is a partial pictorial, partial schematic, illustration of aself-propelled agricultural harvester 100. In the illustrated example,agricultural harvester 100 is a combine harvester. Further, althoughcombine harvesters are provided as examples throughout the presentdisclosure, it will be appreciated that the present description is alsoapplicable to other types of harvesters, such as cotton harvesters,sugarcane harvesters, self-propelled forage harvesters, windrowers, orother agricultural work machines. Consequently, the present disclosureis intended to encompass the various types of harvesters described andis, thus, not limited to combine harvesters. Moreover, the presentdisclosure is directed to other types of work machines, such asagricultural seeders and sprayers, construction equipment, forestryequipment, and turf management equipment where generation of apredictive map may be applicable. Consequently, the present disclosureis intended to encompass these various types of harvesters and otherwork machines and is, thus, not limited to combine harvesters.

As shown in FIG. 1, agricultural harvester 100 illustratively includesan operator compartment 101, which can have a variety of differentoperator interface mechanisms, for controlling agricultural harvester100. Agricultural harvester 100 includes front-end equipment, such as aheader 102, and a cutter generally indicated at 104. Agriculturalharvester 100 also includes a feeder house 106, a feed accelerator 108,and a thresher generally indicated at 110. The feeder house 106 and thefeed accelerator 108 form part of a material handling subsystem 125.Header 102 is pivotally coupled to a frame 103 of agricultural harvester100 along pivot axis 105. One or more actuators 107 drive movement ofheader 102 about axis 105 in the direction generally indicated by arrow109. Thus, a vertical position of header 102 (the header height) aboveground 111 over which the header 102 travels is controllable byactuating actuator 107. While not shown in FIG. 1, agriculturalharvester 100 may also include one or more actuators that operate toapply a tilt angle, a roll angle, or both to the header 102 or portionsof header 102. Tilt refers to an angle at which the cutter 104 engagesthe crop. The tilt angle is increased, for example, by controllingheader 102 to point a distal edge 113 of cutter 104 more toward theground. The tilt angle is decreased by controlling header 102 to pointthe distal edge 113 of cutter 104 more away from the ground. The rollangle refers to the orientation of header 102 about the front-to-backlongitudinal axis of agricultural harvester 100.

Thresher 110 illustratively includes a threshing rotor 112 and a set ofconcaves 114. Further, agricultural harvester 100 also includes aseparator 116. Agricultural harvester 100 also includes a cleaningsubsystem or cleaning shoe (collectively referred to as cleaningsubsystem 118) that includes a cleaning fan 120, chaffer 122, and sieve124. The material handling subsystem 125 also includes discharge beater126, tailings elevator 128, clean grain elevator 130, as well asunloading auger 134 and spout 136. The clean grain elevator moves cleangrain into clean grain tank 132. Agricultural harvester 100 alsoincludes a residue subsystem 138 that can include chopper 140 andspreader 142. Agricultural harvester 100 also includes a propulsionsubsystem that includes an engine that drives ground engaging components144, such as wheels or tracks. In some examples, a combine harvesterwithin the scope of the present disclosure may have more than one of anyof the subsystems mentioned above. In some examples, agriculturalharvester 100 may have left and right cleaning subsystems, separators,etc., which are not shown in FIG. 1.

In operation, and by way of overview, agricultural harvester 100illustratively moves through a field in the direction indicated by arrow147. As agricultural harvester 100 moves, header 102 (and the associatedreel 164) engages the crop to be harvested and gathers the crop towardcutter 104. An operator of agricultural harvester 100 can be a localhuman operator, a remote human operator, or an automated system. Anoperator command is a command by an operator. The operator ofagricultural harvester 100 may determine one or more of a heightsetting, a tilt angle setting, or a roll angle setting for header 102.For example, the operator inputs a setting or settings to a controlsystem, described in more detail below, that controls actuator 107. Thecontrol system may also receive a setting from the operator forestablishing the tilt angle and roll angle of the header 102 andimplement the inputted settings by controlling associated actuators, notshown, that operate to change the tilt angle and roll angle of theheader 102. The actuator 107 maintains header 102 at a height aboveground 111 based on a height setting and, where applicable, at desiredtilt and roll angles. Each of the height, roll, and tilt settings may beimplemented independently of the others. The control system responds toheader error (e.g., the difference between the height setting andmeasured height of header 104 above ground 111 and, in some examples,tilt angle and roll angle errors) with a responsiveness that isdetermined based on a selected sensitivity level. If the sensitivitylevel is set at a greater level of sensitivity, the control systemresponds to smaller header position errors, and attempts to reduce thedetected errors more quickly than when the sensitivity is at a lowerlevel of sensitivity.

Returning to the description of the operation of agricultural harvester100, after crops are cut by cutter 104, the severed crop material ismoved through a conveyor in feeder house 106 toward feed accelerator108, which accelerates the crop material into thresher 110. The cropmaterial is threshed by rotor 112 rotating the crop against concaves114. The threshed crop material is moved by a separator rotor inseparator 116 where a portion of the residue is moved by dischargebeater 126 toward the residue subsystem 138. The portion of residuetransferred to the residue subsystem 138 is chopped by residue chopper140 and spread on the field by spreader 142. In other configurations,the residue is released from the agricultural harvester 100 in awindrow. In other examples, the residue subsystem 138 can include weedseed eliminators (not shown) such as seed baggers or other seedcollectors, or seed crushers or other seed destroyers.

Grain falls to cleaning subsystem 118. Chaffer 122 separates some largerpieces of material from the grain, and sieve 124 separates some of finerpieces of material from the clean grain. Clean grain falls to an augerthat moves the grain to an inlet end of clean grain elevator 130, andthe clean grain elevator 130 moves the clean grain upwards, depositingthe clean grain in clean grain tank 132. Residue is removed from thecleaning subsystem 118 by airflow generated by cleaning fan 120.Cleaning fan 120 directs air along an airflow path upwardly through thesieves and chaffers. The airflow carries residue rearwardly inagricultural harvester 100 toward the residue subsystem 138.

Tailings elevator 128 returns tailings to thresher 110 where thetailings are re-threshed. Alternatively, the tailings also may be passedto a separate re-threshing mechanism by a tailings elevator or anothertransport device where the tailings are re-threshed as well.

FIG. 1 also shows that, in one example, agricultural harvester 100includes ground speed sensor 146, one or more separator loss sensors148, a clean grain camera 150, a forward looking image capture mechanism151, which may be in the form of a stereo or mono camera, and one ormore loss sensors 152 provided in the cleaning subsystem 118.

Ground speed sensor 146 senses the travel speed of agriculturalharvester 100 over the ground. Ground speed sensor 146 may sense thetravel speed of the agricultural harvester 100 by sensing the speed ofrotation of the ground engaging components (such as wheels or tracks), adrive shaft, an axel, or other components. In some instances, the travelspeed may be sensed using a positioning system, such as a globalpositioning system (GPS), a dead reckoning system, a long rangenavigation (LORAN) system, or a wide variety of other systems or sensorsthat provide an indication of travel speed.

Loss sensors 152 illustratively provide an output signal indicative ofthe quantity of grain loss occurring in both the right and left sides ofthe cleaning subsystem 118. In some examples, sensors 152 are strikesensors which count grain strikes per unit of time or per unit ofdistance traveled to provide an indication of the grain loss occurringat the cleaning subsystem 118. The strike sensors for the right and leftsides of the cleaning subsystem 118 may provide individual signals or acombined or aggregated signal. In some examples, sensors 152 may includea single sensor as opposed to separate sensors provided for eachcleaning subsystem 118.

Separator loss sensor 148 provides a signal indicative of grain loss inthe left and right separators, not separately shown in FIG. 1. Theseparator loss sensors 148 may be associated with the left and rightseparators and may provide separate grain loss signals or a combined oraggregate signal. In some instances, sensing grain loss in theseparators may also be performed using a wide variety of different typesof sensors as well.

Agricultural harvester 100 may also include other sensors andmeasurement mechanisms. For instance, agricultural harvester 100 mayinclude one or more of the following sensors: a header height sensorthat senses a height of header 102 above ground 111; stability sensorsthat sense oscillation or bouncing motion (and amplitude) ofagricultural harvester 100; a residue setting sensor that is configuredto sense whether agricultural harvester 100 is configured to chop theresidue, produce a windrow, etc.; a cleaning shoe fan speed sensor tosense the speed of cleaning fan 120; a concave clearance sensor thatsenses clearance between the rotor 112 and concaves 114; a threshingrotor speed sensor that senses a rotor speed of rotor 112; a chafferclearance sensor that senses the size of openings in chaffer 122; asieve clearance sensor that senses the size of openings in sieve 124; amaterial other than grain (MOG) moisture sensor that senses a moisturelevel of the MOG passing through agricultural harvester 100; one or moremachine setting sensors configured to sense various configurablesettings of agricultural harvester 100; a machine orientation sensorthat senses the orientation of agricultural harvester 100; and cropproperty sensors that sense a variety of different types of cropproperties, such as crop type, crop moisture, and other crop properties.Crop property sensors may also be configured to sense characteristics ofthe severed crop material as the crop material is being processed byagricultural harvester 100. For example, in some instances, the cropproperty sensors may sense grain quality such as broken grain, MOGlevels; grain constituents such as starches and protein; and grain feedrate as the grain travels through the feeder house 106, clean grainelevator 130, or elsewhere in the agricultural harvester 100. The cropproperty sensors may also sense the feed rate of biomass through feederhouse 106, through the separator 116 or elsewhere in agriculturalharvester 100. The crop property sensors may also sense the feed rate asa mass flow rate of grain through elevator 130 or through other portionsof the agricultural harvester 100 or provide other output signalsindicative of other sensed variables. Crop property sensors can includeone or more yield sensors that sense crop yield being harvested byagricultural harvester.

Yield sensor(s) can include a grain flow sensor that detects a flow ofcrop, such as grain, in material handling subsystem 125 or otherportions of agricultural harvester 100. For example, a yield sensor caninclude a gamma ray attenuation sensor that measures flow rate ofharvested grain. In another example, a yield sensor includes an impactplate sensor that detects impact of grain against a sensing plate orsurface so as to measure mass flow rate of harvested grain. In anotherexample, a yield sensor includes one or more load cells which measure ordetect a load or mass of harvested grain. For example, one or more loadcells may be located at a bottom of grain tank 132, wherein changes inthe weight or mass of grain within grain tank 132 during a measurementinterval indicates the aggregate yield during the measurement interval.The measurement interval may be increased for averaging or decreased formore instantaneous measurements. In another example, a yield sensorincludes cameras or optical sensing devices that detect the size orshape of an aggregated mass of harvested grain, such as the shape of themound or height of a mound of grain in grain tank 132. The change inshape or height of the mound during the measurement interval indicatesan aggregate yield during the measurement interval. In other examples,other yield sensing technologies are employed. For instance, in oneexample, a yield sensor includes two or more of the above describedsensors, and the yield for a measurement interval is determined fromsignals output by each of the multiple different types of sensors. Forexample, yield is determined based upon signals from a gamma rayattenuation sensor, an impact plate sensor, load cells within grain tank132, and optical sensors along grain tank 132.

Prior to describing how agricultural harvester 100 generates afunctional predictive yield map and uses the functional predictive yieldmap for presentation or control, a brief description of some of theitems on agricultural harvester 100, and their operation, will first bedescribed. The description of FIGS. 2 and 3 describe receiving a generaltype of prior information map and combining information from the priorinformation map with a georeferenced sensor signal generated by anin-situ sensor, where the sensor signal is indicative of acharacteristic in the field, such as characteristics of crop or weedspresent in the field. Characteristics of the field may include, but arenot limited to, characteristics of a field such as slope, weedintensity, weed type, soil moisture, surface quality; characteristics ofcrop properties such as crop height, crop moisture, crop density, cropstate; characteristics of grain properties such as grain moisture, grainsize, grain test weight; and characteristics of machine performance suchas loss levels, job quality, fuel consumption, and power utilization. Arelationship between the characteristic values obtained from in-situsensor signals and the prior information map values is identified, andthat relationship is used to generate a new functional predictive map. Afunctional predictive map predicts values at different geographiclocations in a field, and one or more of those values may be used forcontrolling a machine, such as one or more subsystems of an agriculturalharvester. In some instances, a functional predictive map can bepresented to a user, such as an operator of an agricultural workmachine, which may be an agricultural harvester. A functional predictivemap may be presented to a user visually, such as via a display,haptically, or audibly. The user may interact with the functionalpredictive map to perform editing operations and other user interfaceoperations. In some instances, a functional predictive map can be usedfor one or more of controlling an agricultural work machine, such as anagricultural harvester, presentation to an operator or other user, andpresentation to an operator or user for interaction by the operator oruser.

After the general approach is described with respect to FIGS. 2 and 3, amore specific approach for generating a functional predictive yield mapthat can be presented to an operator or user, or used to controlagricultural harvester 100, or both is described with respect to FIGS. 4and 5. Again, while the present discussion proceeds with respect to theagricultural harvester and, particularly, a combine harvester, the scopeof the present disclosure encompasses other types of agriculturalharvesters or other agricultural work machines.

FIG. 2 is a block diagram showing some portions of an exampleagricultural harvester 100. FIG. 2 shows that agricultural harvester 100illustratively includes one or more processors or servers 201, datastore 202, geographic position sensor 204, communication system 206, andone or more in-situ sensors 208 that sense one or more agriculturalcharacteristics of a field concurrent with a harvesting operation. Anagricultural characteristic can include any characteristic that can havean effect of the harvesting operation. Some examples of agriculturalcharacteristics include characteristics of the harvesting machine, thefield, the plants on the field, and the weather. Other types ofagricultural characteristics are also included. The in-situ sensors 208generate values corresponding to the sensed characteristics. Theagricultural harvester 100 also includes a predictive model orrelationship generator (collectively referred to hereinafter as“predictive model generator 210”), predictive map generator 212, controlzone generator 213, control system 214, one or more controllablesubsystems 216, and an operator interface mechanism 218. Theagricultural harvester 100 can also include a wide variety of otheragricultural harvester functionality 220. The in-situ sensors 208include, for example, on-board sensors 222, remote sensors 224, andother sensors 226 that sense characteristics of a field during thecourse of an agricultural operation. Predictive model generator 210illustratively includes a prior information variable-to-in-situ variablemodel generator 228, and predictive model generator 210 can includeother items 230. Control system 214 includes communication systemcontroller 229, operator interface controller 231, a settings controller232, path planning controller 234, feed rate controller 236, header andreel controller 238, draper belt controller 240, deck plate positioncontroller 242, residue system controller 244, machine cleaningcontroller 245, zone controller 247, and control system 214 can includeother items 246. Controllable subsystems 216 include machine and headeractuators 248, propulsion subsystem 250, steering subsystem 252, residuesubsystem 138, machine cleaning subsystem 254, and controllablesubsystems 216 can include a wide variety of other subsystems 256.

FIG. 2 also shows that agricultural harvester 100 can receive one ormore prior information map(s) 258. As described below, the priorinformation map(s) include, for example, a vegetative index map or avegetation map from a prior operation in the field. However, priorinformation map(s) 258 may also encompass other types of data that wereobtained prior to a harvesting operation or a map from a prioroperation, such as historical yield maps from past years that containcontextual information associated with the historical yield. Contextualinformation can include, without limitation, one or more of weatherconditions over a growing season, presence of pests, geographiclocation, soil types, irrigation, treatment application, etc. Weatherconditions can include, without limitation, precipitation over theseason, presence of hail capable of crop damage, presence of high winds,temperature over the season, etc. Some examples of pests broadlyinclude, insects, fungi, weeds, bacteria, viruses, etc. Some examples oftreatment applications include herbicide, pesticide, fungicide,fertilizer, mineral supplements, etc. FIG. 2 also shows that an operator260 may operate the agricultural harvester 100. The operator 260interacts with operator interface mechanisms 218. In some examples,operator interface mechanisms 218 may include joysticks, levers, asteering wheel, linkages, pedals, buttons, dials, keypads, useractuatable elements (such as icons, buttons, etc.) on a user interfacedisplay device, a microphone and speaker (where speech recognition andspeech synthesis are provided), among a wide variety of other types ofcontrol devices. Where a touch sensitive display system is provided,operator 260 may interact with operator interface mechanisms 218 usingtouch gestures. These examples described above are provided asillustrative examples and are not intended to limit the scope of thepresent disclosure. Consequently, other types of operator interfacemechanisms 218 may be used and are within the scope of the presentdisclosure.

Prior information map 258 may be downloaded onto agricultural harvester100 and stored in data store 202, using communication system 206 or inother ways. In some examples, communication system 206 may be a cellularcommunication system, a system for communicating over a wide areanetwork or a local area network, a system for communicating over a nearfield communication network, or a communication system configured tocommunicate over any of a variety of other networks or combinations ofnetworks. Communication system 206 may also include a system thatfacilitates downloads or transfers of information to and from a securedigital (SD) card or a universal serial bus (USB) card, or both.

Geographic position sensor 204 illustratively senses or detects thegeographic position or location of agricultural harvester 100.Geographic position sensor 204 can include, but is not limited to, aglobal navigation satellite system (GNSS) receiver that receives signalsfrom a GNSS satellite transmitter. Geographic position sensor 204 canalso include a real-time kinematic (RTK) component that is configured toenhance the precision of position data derived from the GNSS signal.Geographic position sensor 204 can include a dead reckoning system, acellular triangulation system, or any of a variety of other geographicposition sensors.

In-situ sensors 208 may be any of the sensors described above withrespect to FIG. 1. In-situ sensors 208 include on-board sensors 222 thatare mounted on-board agricultural harvester 100. Such sensors mayinclude, for instance, an impact plate sensor, a radiation attenuationsensor, or an image sensor that is internal to agricultural harvester100 (such as a clean grain camera). The in-situ sensors 208 may alsoinclude remote in-situ sensors 224 that capture in-situ information.In-situ data include data taken from a sensor on-board the agriculturalharvester or taken by any sensor where the data are detected during theharvesting operation.

After being retrieved by agricultural harvester 100, prior informationmap selector 209 can filter or select one or more specific priorinformation map(s) 258 for usage by predictive model generator 210. Inone example, prior information map selector 209 selects a map based on acomparison of the contextual information in the prior information mapversus the present contextual information. For example, a historicalyield map may be selected from one of the past years where weatherconditions over the growing season were similar to the present year'sweather conditions. Or, for example, a historical yield map may beselected from one of the past years when the context information is notsimilar. For example, a historical yield map may be selected for a prioryear that was “dry” (i.e., had drought conditions or reducedprecipitation), while the present year is “wet” (i.e., had increasedprecipitation or flood conditions). There still may be a usefulhistorical relationship, but the relationship may be inverse. Forinstance, areas that are flooded in a wet year may be areas of higheryield in a dry year because these areas may retain more water in dryyears. Present contextual information may include contextual informationbeyond immediate contextual information. For instance, presentcontextual information can include, but not by limitation, a set ofinformation corresponding to the present growing season, a set of datacorresponding to a winter before the current growing season, or a set ofdata corresponding to several past years, amongst others.

The contextual information can also be used for correlations betweenareas with similar contextual characteristics, regardless of whether thegeographic position corresponds to the same position on priorinformation map 258. For instance, historical yield values from areawith similar soil types in other fields can be used as prior informationmap 258 to create the predictive yield map. For example, the contextualcharacteristic information associated with a different location may beapplied to the location on the prior information map 258 having similarcharacteristic information.

Predictive model generator 210 generates a model that is indicative of arelationship between the values sensed by the in-situ sensor 208 and acharacteristic mapped to the field by the prior information map 258. Forexample, if the prior information map 258 maps a vegetative index valueto different locations in the field, and the in-situ sensor 208 issensing a value indicative of yield, then prior informationvariable-to-in-situ variable model generator 228 generates a predictiveyield model that models the relationship between the vegetative indexvalues and the yield values. Then, predictive map generator 212 uses thepredictive yield model generated by predictive model generator 210 togenerate a functional predictive yield map that predicts the value ofyield, at different locations in the field, based upon the priorinformation map 258. Or, for example, if the prior information map 258maps a historical yield value to different locations in the field andthe in-situ sensor 208 is sensing a value indicative of yield, thenprior information variable-to-in-situ variable model generator 228generates a predictive yield model that models the relationship betweenthe historical yield values (with or without contextual information) andthe in-situ yield values. Then, predictive map generator 212 uses thepredictive yield model generated by predictive model generator 210 togenerate a functional predictive yield map that predicts the value ofyield that is expected be sensed by the in-situ sensors 208, atdifferent locations in the field, based upon the prior information map258.

In some examples, the type of data in the functional predictive map 263may be the same as the in-situ data type sensed by the in-situ sensors208. In some instances, the type of data in the functional predictivemap 263 may have different units from the data sensed by the in-situsensors 208. In some examples, the type of data in the functionalpredictive map 263 may be different from the data type sensed by thein-situ sensors 208 but has a relationship to data type sensed by thein-situ sensors 208. For example, in some examples, the in-situ datatype may be indicative of the type of data in the functional predictivemap 263. In some examples, the type of data in the functional predictivemap 263 may be different than the data type in the prior information map258. In some instances, the type of data in the functional predictivemap 263 may have different units from the data in the prior informationmap 258. In some examples, the type of data in the functional predictivemap 263 may be different from the data type in the prior information map258 but has a relationship to the data type in the prior information map258. For example, in some examples, the data type in the priorinformation map 258 may be indicative of the type of data in thefunctional predictive map 263. In some examples, the type of data in thefunctional predictive map 263 is different than one of, or both of thein-situ data type sensed by the in-situ sensors 208 and the data type inthe prior information map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of, or both of, of thein-situ data type sensed by the in-situ sensors 208 and the data type inprior information map 258. In some examples, the type of data in thefunctional predictive map 263 is the same as one of the in-situ datatype sensed by the in-situ sensors 208 or the data type in the priorinformation map 258, and different than the other.

Continuing with the preceding vegetative index example, predictive mapgenerator 212 can use the vegetative index values in prior informationmap 258 and the model generated by predictive model generator 210 togenerate a functional predictive map 263 that predicts the yield atdifferent locations in the field. Predictive map generator 212 thusoutputs predictive map 264.

As shown in FIG. 2, predictive map 264 predicts the value of acharacteristic, which may be the same characteristic sensed by in-situsensor(s) 208, or a characteristic related to the characteristic sensedby the in-situ sensor(s) 208, at various locations across the fieldbased upon a prior information value in prior information map 258 atthose locations (or locations with similar contextual information, evenif in a different field) and using the predictive model. For example, ifpredictive model generator 210 has generated a predictive modelindicative of a relationship between a vegetative index value and yield,then, given the vegetative index value at different locations across thefield, predictive map generator 212 generates a predictive map 264 thatpredicts the value of the yield at different locations across the field.The vegetative index value, obtained from the prior information map 258,at those locations and the relationship between vegetative index valueand yield, obtained from the predictive model, are used to generate thepredictive map 264.

Some variations in the data types that are mapped in the priorinformation map 258, the data types sensed by in-situ sensors 208 andthe data types predicted on the predictive map 264 will now bedescribed.

In some examples, the data type in the prior information map 258 isdifferent from the data type sensed by in-situ sensors 208, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 208. For instance, the prior information map 258 maybe a vegetative index map, and the variable sensed by the in-situsensors 208 may be yield. The predictive map 264 may then be apredictive yield map that maps predicted yield values to differentgeographic locations in the field. In another example, the priorinformation map 258 may be a vegetative index map, and the variablesensed by the in-situ sensors 208 may be crop height. The predictive map264 may then be a predictive crop height map that maps predicted cropheight values to different geographic locations in the field.

Also, in some examples, the data type in the prior information map 258is different from the data type sensed by in-situ sensors 208, and thedata type in the predictive map 264 is different from both the data typein the prior information map 258 and the data type sensed by the in-situsensors 208. For instance, the prior information map 258 may be avegetative index map, and the variable sensed by the in-situ sensors 208may be crop height. The predictive map 264 may then be a predictivebiomass map that maps predicted biomass values to different geographiclocations in the field. In another example, the prior information map258 may be a vegetative index map, and the variable sensed by thein-situ sensors 208 may be yield. The predictive map 264 may then be apredictive speed map that maps predicted harvester speed values todifferent geographic locations in the field.

In some examples, the prior information map 258 is from a prior passthrough the field during a prior operation and the data type isdifferent from the data type sensed by in-situ sensors 208, yet the datatype in the predictive map 264 is the same as the data type sensed bythe in-situ sensors 208. For instance, the prior information map 258 maybe a seed population map generated during planting, and the variablesensed by the in-situ sensors 208 may be stalk size. The predictive map264 may then be a predictive stalk size map that maps predicted stalksize values to different geographic locations in the field. In anotherexample, the prior information map 258 may be a seeding hybrid map, andthe variable sensed by the in-situ sensors 208 may be crop state such asstanding crop or down crop. The predictive map 264 may then be apredictive crop state map that maps predicted crop state values todifferent geographic locations in the field.

In some examples, the prior information map 258 is from a prior passthrough the field during a prior operation and the data type is the sameas the data type sensed by in-situ sensors 208, and the data type in thepredictive map 264 is also the same as the data type sensed by thein-situ sensors 208. For instance, the prior information map 258 may bea yield map generated during a previous year, and the variable sensed bythe in-situ sensors 208 may be yield. The predictive map 264 may then bea predictive yield map that maps predicted yield values to differentgeographic locations in the field. In such an example, the relativeyield differences in the georeferenced prior information map 258 fromthe prior year can be used by predictive model generator 210 to generatea predictive model that models a relationship between the relative yielddifferences on the prior information map 258 and the yield values sensedby in-situ sensors 208 during the current harvesting operation. Thepredictive model is then used by predictive map generator 212 togenerate a predictive yield map.

In another example, the prior information map 258 may be a weedintensity map generated during a prior operation, such as from asprayer, and the variable sensed by the in-situ sensors 208 may be weedintensity. The predictive map 264 may then be a predictive weedintensity map that maps predicted weed intensity values to differentgeographic locations in the field. In such an example, a map of the weedintensities at time of spraying is geo-referenced recorded and providedto agricultural harvester 100 as a prior information map 258 of weedintensity. In-situ sensors 208 can detect weed intensity at geographiclocations in the field and predictive model generator 210 may then builda predictive model that models a relationship between weed intensity attime of harvest and weed intensity at time of spraying. This is becausethe sprayer will have impacted the weed intensity at time of spraying,but weeds may still crop up in similar areas again by harvest. However,the weed areas at harvest are likely to have different intensity basedon timing of the harvest, weather, weed type, among other things.

In some examples, predictive map 264 can be provided to the control zonegenerator 213. Control zone generator 213 groups adjacent portions of anarea into one or more control zones based on data values of predictivemap 264 that are associated with those adjacent portions. A control zonemay include two or more contiguous portions of an area, such as a field,for which a control parameter corresponding to the control zone forcontrolling a controllable subsystem is constant. For example, aresponse time to alter a setting of controllable subsystems 216 may beinadequate to satisfactorily respond to changes in values contained in amap, such as predictive map 264. In that case, control zone generator213 parses the map and identifies control zones that are of a definedsize to accommodate the response time of the controllable subsystems216. In another example, control zones may be sized to reduce wear fromexcessive actuator movement resulting from continuous adjustment. Insome examples, there may be a different set of control zones for eachcontrollable subsystem 216 or for groups of controllable subsystems 216.The control zones may be added to the predictive map 264 to obtainpredictive control zone map 265. Predictive control zone map 265 canthus be similar to predictive map 264 except that predictive controlzone map 265 includes control zone information defining the controlzones. Thus, a functional predictive map 263, as described herein, mayor may not include control zones. Both predictive map 264 and predictivecontrol zone map 265 are functional predictive maps 263. In one example,a functional predictive map 263 does not include control zones, such aspredictive map 264. In another example, a functional predictive map 263does include control zones, such as predictive control zone map 265. Insome examples, multiple crops may be simultaneously present in a fieldif an intercrop production system is implemented. In that case,predictive map generator 212 and control zone generator 213 are able toidentify the location and characteristics of the two or more crops andthen generate predictive map 264 and predictive control zone map 265accordingly.

It will also be appreciated that control zone generator 213 can clustervalues to generate control zones and the control zones can be added topredictive control zone map 265, or a separate map, showing only thecontrol zones that are generated. In some examples, the control zonesmay be used for controlling or calibrating agricultural harvester 100 orboth. In other examples, the control zones may be presented to theoperator 260 and used to control or calibrate agricultural harvester100, and, in other examples, the control zones may be presented to theoperator 260 or another user or stored for later use.

Predictive map 264 or predictive control zone map 265 or both areprovided to control system 214, which generates control signals basedupon the predictive map 264 or predictive control zone map 265 or both.In some examples, communication system controller 229 controlscommunication system 206 to communicate the predictive map 264 orpredictive control zone map 265 or control signals based on thepredictive map 264 or predictive control zone map 265 to otheragricultural harvesters that are harvesting in the same field. In someexamples, communication system controller 229 controls the communicationsystem 206 to send the predictive map 264, predictive control zone map265, or both to other remote systems.

Operator interface controller 231 is operable to generate controlsignals to control operator interface mechanisms 218. The operatorinterface controller 231 is also operable to present the predictive map264 or predictive control zone map 265 or other information derived fromor based on the predictive map 264, predictive control zone map 265, orboth to operator 260. Operator 260 may be a local operator or a remoteoperator. As an example, controller 231 generates control signals tocontrol a display mechanism to display one or both of predictive map 264and predictive control zone map 265 for the operator 260. Controller 231may generate operator actuatable mechanisms that are displayed and canbe actuated by the operator to interact with the displayed map. Theoperator can edit the map by, for example, correcting a yield valuedisplayed on the map based on the operator's observation. Settingscontroller 232 can generate control signals to control various settingson the agricultural harvester 100 based upon predictive map 264, thepredictive control zone map 265, or both. For instance, settingscontroller 232 can generate control signals to control machine andheader actuators 248. In response to the generated control signals, themachine and header actuators 248 operate to control, for example, one ormore of the sieve and chaffer settings, thresher clearance, rotorsettings, cleaning fan speed settings, header height, headerfunctionality, reel speed, reel position, draper functionality (whereagricultural harvester 100 is coupled to a draper header), corn headerfunctionality, internal distribution control, and other actuators 248that affect the other functions of the agricultural harvester 100. Pathplanning controller 234 illustratively generates control signals tocontrol steering subsystem 252 to steer agricultural harvester 100according to a desired path. Path planning controller 234 can control apath planning system to generate a route for agricultural harvester 100and can control propulsion subsystem 250 and steering subsystem 252 tosteer agricultural harvester 100 along that route. Feed rate controller236 can control various subsystems, such as propulsion subsystem 250 andmachine actuators 248, to control a feed rate based upon the predictivemap 264 or predictive control zone map 265 or both. For instance, asagricultural harvester 100 approaches an area yielding above a selectedthreshold, feed rate controller 236 may reduce the speed of agriculturalharvester 100 to maintain constant feed rate of grain or biomass throughthe machine. Header and reel controller 238 can generate control signalsto control a header or a reel or other header functionality. Draper beltcontroller 240 can generate control signals to control a draper belt orother draper functionality based upon the predictive map 264, predictivecontrol zone map 265, or both. Deck plate position controller 242 cangenerate control signals to control a position of a deck plate includedon a header based on predictive map 264 or predictive control zone map265 or both, and residue system controller 244 can generate controlsignals to control a residue subsystem 138 based upon predictive map 264or predictive control zone map 265, or both. Machine cleaning controller245 can generate control signals to control machine cleaning subsystem254. For instance, based upon the different types of seeds or weedspassed through agricultural harvester 100, a particular type of machinecleaning operation or a frequency with which a cleaning operation isperformed may be controlled. Other controllers included on theagricultural harvester 100 can control other subsystems based on thepredictive map 264 or predictive control zone map 265 or both as well.

FIGS. 3A and 3B (collectively referred to herein as FIG. 3) show a flowdiagram illustrating one example of the operation of agriculturalharvester 100 in generating a predictive map 264 and predictive controlzone map 265 based upon prior information map 258.

At 280, agricultural harvester 100 receives prior information map 258.Examples of prior information map 258 or receiving prior information map258 are discussed with respect to blocks 281, 282, 284 and 286. Asdiscussed above, prior information map 258 maps values of a variable,corresponding to a first characteristic, to different locations in thefield, as indicated at block 282. For instance, one prior informationmap may be a seeding map generated during a prior operation or based ondata from a prior operation on the field, such as prior seed plantingoperation performed by a seeder. The data for the prior information map258 may be collected in other ways as well. For instance, the data maybe collected based on aerial images or measured values taken during aprevious year, or earlier in the current growing season, or at othertimes. The information may be based on data detected or gathered inother ways (other than using aerial images) as well. For instance, thedata for the prior information map 258 can be transmitted toagricultural harvester 100 using communication system 206 and stored indata store 202. The data for the prior information map 258 can beprovided to agricultural harvester 100 using communication system 206 inother ways as well, and this is indicated by block 286 in the flowdiagram of FIG. 3. In some examples, the prior information map 258 canbe received by communication system 206.

At block 287, prior information map selector 209 can select one or moremaps from the plurality of candidate prior information maps received inblock 280. For example, multiple years of historical yield maps may bereceived as candidate prior information maps. Each of these maps cancontain contextual information such as weather patterns over a period oftime, such as a year, pest surges over a period of time, such as a year,soil types, etc. Contextual information can be used to select whichhistorical yield map should be selected. For instance, the weatherconditions over a period of time, such in a current year, or the soiltypes for the current field can be compared to the weather conditionsand soil type in the contextual information for each candidate priorinformation map. The results of such a comparison can be used to selectwhich historical yield map should be selected. For example, years withsimilar weather conditions may generally produce similar yields or yieldtrends across a field. In some cases, years with opposite weatherconditions may also be useful for predicting yield based on historicalyield. For instance, an area with a high yield in a dry year, might havea low yield in a wet year as the area gets flooded. The process by whichone or more prior information maps are selected by prior information mapselector 209 can be manual, semi-automated or automated. In someexamples, during a harvesting operation, prior information map selector209 can continually or intermittently determine whether a differentprior information map has a better relationship with the in-situ sensorvalue. If a different prior information map is correlating with thein-situ data more closely, then prior information map selector 209 canreplace the currently selected prior information map with the morecorrelative prior information map.

Upon commencement of a harvesting operation, in-situ sensors 208generate sensor signals indicative of one or more in-situ data valuesindicative of a plant characteristic, such as a yield, as indicated byblock 288. Examples of in-situ sensors 288 are discussed with respect toblocks 222, 290, and 226. As explained above, the in-situ sensors 208include on-board sensors 222; remote in-situ sensors 224, such asUAV-based sensors flown at a time to gather in-situ data, shown in block290; or other types of in-situ sensors, designated by in-situ sensors226. In some examples, data from on-board sensors is georeferenced usingposition heading or speed data from geographic position sensor 204.

Predictive model generator 210 controls the prior informationvariable-to-in-situ variable model generator 228 to generate a modelthat models a relationship between the mapped values contained in theprior information map 258 and the in-situ values sensed by the in-situsensors 208 as indicated by block 292. The characteristics or data typesrepresented by the mapped values in the prior information map 258 andthe in-situ values sensed by the in-situ sensors 208 may be the samecharacteristics or data type or different characteristics or data types.

The relationship or model generated by predictive model generator 210 isprovided to predictive map generator 212. Predictive map generator 212generates a predictive map 264 that predicts a value of thecharacteristic sensed by the in-situ sensors 208 at different geographiclocations in a field being harvested, or a different characteristic thatis related to the characteristic sensed by the in-situ sensors 208,using the predictive model and the prior information map 258, asindicated by block 294.

It should be noted that, in some examples, the prior information map 258may include two or more different maps or two or more different maplayers of a single map. Each map layer may represent a different datatype from the data type of another map layer or the map layers may havethe same data type that were obtained at different times. Each map inthe two or more different maps or each layer in the two or moredifferent map layers of a map maps a different type of variable to thegeographic locations in the field. In such an example, predictive modelgenerator 210 generates a predictive model that models the relationshipbetween the in-situ data and each of the different variables mapped bythe two or more different maps or the two or more different map layers.Similarly, the in-situ sensors 208 can include two or more sensors eachsensing a different type of variable. Thus, the predictive modelgenerator 210 generates a predictive model that models the relationshipsbetween each type of variable mapped by the prior information map 258and each type of variable sensed by the in-situ sensors 208. Predictivemap generator 212 can generate a functional predictive map 263 thatpredicts a value for each sensed characteristic sensed by the in-situsensors 208 (or a characteristic related to the sensed characteristic)at different locations in the field being harvested using the predictivemodel and each of the maps or map layers in the prior information map258.

Predictive map generator 212 configures the predictive map 264 so thatthe predictive map 264 is actionable (or consumable) by control system214. Predictive map generator 212 can provide the predictive map 264 tothe control system 214 or to control zone generator 213 or both. Someexamples of different ways in which the predictive map 264 can beconfigured or output are described with respect to blocks 296, 295, 299and 297. For instance, predictive map generator 212 configurespredictive map 264 so that predictive map 264 includes values that canbe read by control system 214 and used as the basis for generatingcontrol signals for one or more of the different controllable subsystemsof the agricultural harvester 100, as indicated by block 296.

Control zone generator 213 can divide the predictive map 264 intocontrol zones based on the values on the predictive map 264.Contiguously-geolocated values that are within a threshold value of oneanother can be grouped into a control zone. The threshold value can be adefault threshold value, or the threshold value can be set based on anoperator input, based on an input from an automated system or based onother criteria. A size of the zones may be based on a responsiveness ofthe control system 214, the controllable subsystems 216, or based onwear considerations, or on other criteria as indicated by block 295.Predictive map generator 212 configures predictive map 264 forpresentation to an operator or other user. Control zone generator 213can configure predictive control zone map 265 for presentation to anoperator or other user. This is indicated by block 299. When presentedto an operator or other user, the presentation of the predictive map 264or predictive control zone map 265 or both may contain one or more ofthe predictive values on the predictive map 264 correlated to geographiclocation, the control zones on predictive control zone map 265correlated to geographic location, and settings values or controlparameters that are used based on the predicted values on predictive map264 or zones on predictive control zone map 265. The presentation can,in another example, include more abstracted information or more detailedinformation. The presentation can also include a confidence level thatindicates an accuracy with which the predictive values on predictive map264 or the zones on predictive control zone map 265 conform to measuredvalues that may be measured by sensors on agricultural harvester 100 asagricultural harvester 100 moves through the field. Further whereinformation is presented to more than one location, anauthentication/authorization system can be provided to implementauthentication and authorization processes. For instance, there may be ahierarchy of individuals that are authorized to view and change maps andother presented information. By way of example, an on-board displaydevice may show the maps in near real time locally on the machine, only,or the maps may also be generated at one or more remote locations. Insome examples, each physical display device at each location may beassociated with a person or a user permission level. The user permissionlevel may be used to determine which display markers are visible on thephysical display device, and which values the corresponding person maychange. As an example, a local operator of agricultural harvester 100may be unable to see the information corresponding to the predictive map264 or make any changes to machine operation. A supervisor, at a remotelocation, however, may be able to see the predictive map 264 on thedisplay, but not make changes. A manager, who may be at a separateremote location, may be able to see all of the elements on predictivemap 264 and also change the predictive map 264 that is used in machinecontrol. This is one example of an authorization hierarchy that may beimplemented. The predictive map 264 or predictive control zone map 265or both can be configured in other ways as well, as indicated by block297.

At block 298, input from geographic position sensor 204 and otherin-situ sensors 208 are received by the control system. Block 300represents receipt by control system 214 of an input from the geographicposition sensor 204 identifying a geographic location of agriculturalharvester 100. Block 302 represents receipt by the control system 214 ofsensor inputs indicative of trajectory or heading of agriculturalharvester 100, and block 304 represents receipt by the control system214 of a speed of agricultural harvester 100. Block 306 representsreceipt by the control system 214 of other information from variousin-situ sensors 208.

At block 308, control system 214 generates control signals to controlthe controllable subsystems 216 based on the predictive map 264 orpredictive control zone map 265 or both and the input from thegeographic position sensor 204 and any other in-situ sensors 208. Atblock 310, control system 214 applies the control signals to thecontrollable subsystems. It will be appreciated that the particularcontrol signals that are generated, and the particular controllablesubsystems 216 that are controlled, may vary based upon one or moredifferent things. For example, the control signals that are generatedand the controllable subsystems 216 that are controlled may be based onthe type of predictive map 264 or predictive control zone map 265 orboth that is being used. Similarly, the control signals that aregenerated and the controllable subsystems 216 that are controlled andthe timing of the control signals can be based on various latencies ofcrop flow through the agricultural harvester 100 and the responsivenessof the controllable subsystems 216.

By way of example, a generated predictive map 264 in the form of apredictive yield map can be used to control one or more controllablesubsystems 216. For example, the functional predictive yield map caninclude yield values georeferenced to locations within the field beingharvested. The functional predictive yield map can be extracted and usedto control the steering and propulsion subsystems 252 and 250. Bycontrolling the steering and propulsion subsystems 252 and 250, a feedrate of material or grain moving through the agricultural harvester 100can be controlled. Similarly, the header height can be controlled totake in more or less material and thus the header height can also becontrolled to control feed rate of material through the agriculturalharvester 100. In other examples, if the predictive map 264 maps a yieldforward of the machine being higher on one portion of the header thananother portion of the header, resulting in a different biomass enteringone side of the header than the other side, control of the header may beimplemented. For example, a draper speed on one side of the header maybe increased or decreased relative to the draper speed other side of theheader to account for the additional biomass. Thus, the header and reelcontroller 238 can be controlled using georeferenced values present inthe predictive yield map to control draper speeds of the draper belts onthe header. The preceding example involving feed rate and header controlusing a functional predictive yield map is provided merely as anexample. Consequently, a wide variety of other control signals can begenerated using values obtained from a predictive yield map or othertype of functional predictive map to control one or more of thecontrollable subsystems 216.

At block 312, a determination is made as to whether the harvestingoperation has been completed. If harvesting is not completed theprocessing advances to block 314 where in-situ sensor data fromgeographic position sensor 204 and in-situ sensors 208 (and perhapsother sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 can alsodetect learning trigger criteria to perform machine learning on one ormore of the predictive map 264, predictive control zone map 265, themodel generated by predictive model generator 210, the zones generatedby control zone generator 213, one or more control algorithmsimplemented by the controllers in the control system 214, and othertriggered learning.

The learning trigger criteria can include any of a wide variety ofdifferent criteria. Some examples of detecting trigger criteria arediscussed with respect to blocks 318, 320, 321, 322 and 324. Forinstance, in some examples, triggered learning can involve recreation ofa relationship used to generate a predictive model when a thresholdamount of in-situ sensor data are obtained from in-situ sensors 208. Insuch examples, receipt of an amount of in-situ sensor data from thein-situ sensors 208 that exceeds a threshold triggers or causes thepredictive model generator 210 to generate a new predictive model thatis used by predictive map generator 212. Thus, as agricultural harvester100 continues a harvesting operation, receipt of the threshold amount ofin-situ sensor data from the in-situ sensors 208 triggers the creationof a new relationship represented by a predictive model generated bypredictive model generator 210. Further, new predictive map 264,predictive control zone map 265, or both can be regenerated using thenew predictive model. Block 318 represents detecting a threshold amountof in-situ sensor data used to trigger creation of a new predictivemodel.

In other examples, the learning trigger criteria may be based on howmuch the in-situ sensor data from the in-situ sensors 208 are changing,such as over time or compared to previous values. For example, ifvariations within the in-situ sensor data (or the relationship betweenthe in-situ sensor data and the information in prior information map258) are within a selected range or is less than a defined amount or isbelow a threshold value, then a new predictive model is not generated bythe predictive model generator 210. As a result, the predictive mapgenerator 212 does not generate a new predictive map 264, predictivecontrol zone map 265, or both. However, if variations within the in-situsensor data are outside of the selected range, are greater than thedefined amount, or are above the threshold value, for example, then thepredictive model generator 210 generates a new predictive model usingall or a portion of the newly received in-situ sensor data that thepredictive map generator 212 uses to generate a new predictive map 264.At block 320, variations in the in-situ sensor data, such as a magnitudeof an amount by which the data exceeds the selected range or a magnitudeof the variation of the relationship between the in-situ sensor data andthe information in the prior information map 258, can be used as atrigger to cause generation of a new predictive model and predictivemap. Keeping with the examples described above, the threshold, therange, and the defined amount can be set to default values; set by anoperator or user interaction through a user interface; set by anautomated system; or set in other ways.

Other learning trigger criteria can also be used. For instance, ifpredictive model generator 210 switches to a different prior informationmap (different from the originally selected prior information map 258),then switching to the different prior information map may triggerrelearning by predictive model generator 210, predictive map generator212, control zone generator 213, control system 214, or other items. Inanother example, transitioning of agricultural harvester 100 to adifferent topography or to a different control zone may be used aslearning trigger criteria as well.

In some instances, operator 260 can also edit the predictive map 264 orpredictive control zone map 265 or both. The edits can change a value onthe predictive map 264; change a size, shape, position, or existence ofa control zone on predictive control zone map 265; or both. Block 321shows that edited information can be used as learning trigger criteria.

In some instances, it may also be that operator 260 observes thatautomated control of a controllable subsystem, is not what the operatordesires. In such instances, the operator 260 may provide a manualadjustment to the controllable subsystem reflecting that the operator260 desires the controllable subsystem to operate in a different waythan is being commanded by control system 214. Thus, manual alterationof a setting by the operator 260 can cause one or more of predictivemodel generator 210 to relearn a model, predictive map generator 212 toregenerate map 264, control zone generator 213 to regenerate one or morecontrol zones on predictive control zone map 265, and control system 214to relearn a control algorithm or to perform machine learning on one ormore of the controller components 232 through 246 in control system 214based upon the adjustment by the operator 260, as shown in block 322.Block 324 represents the use of other triggered learning criteria.

In other examples, relearning may be performed periodically orintermittently based, for example, upon a selected time interval such asa discrete time interval or a variable time interval, as indicated byblock 326.

If relearning is triggered, whether based upon learning trigger criteriaor based upon passage of a time interval, as indicated by block 326,then one or more of the predictive model generator 210, predictive mapgenerator 212, control zone generator 213, and control system 214performs machine learning to generate a new predictive model, a newpredictive map, a new control zone, and a new control algorithm,respectively, based upon the learning trigger criteria. The newpredictive model, the new predictive map, and the new control algorithmare generated using any additional data that has been collected sincethe last learning operation was performed. Performing relearning isindicated by block 328.

If the harvesting operation has been completed, operation moves fromblock 312 to block 330 where one or more of the predictive map 264,predictive control zone map 265, and predictive model generated bypredictive model generator 210 are stored. The predictive map 264,predictive control zone map 265, and predictive model may be storedlocally on data store 202 or sent to a remote system using communicationsystem 206 for later use.

It will be noted that while some examples herein describe predictivemodel generator 210 and predictive map generator 212 receiving a priorinformation map in generating a predictive model and a functionalpredictive map, respectively, in other examples, the predictive modelgenerator 210 and predictive map generator 212 can receive, ingenerating a predictive model and a functional predictive map,respectively other types of maps, including predictive maps, such as afunctional predictive map generated during the harvesting operation.

FIG. 4 is a block diagram of a portion of the agricultural harvester 100shown in FIG. 1. Particularly, FIG. 4 shows, among other things,examples of the predictive model generator 210 and the predictive mapgenerator 212 in more detail. FIG. 4 also illustrates information flowamong the various components shown therein. As shown, the predictivemodel generator 210 receives one or more of a vegetative index map 332or a historical yield map 333 as a prior information map. Historicalyield map 333 includes historical yield values 335 indicative of yieldvalues across the field during a past harvest. Historical yield map 333also includes contextual data 337 that is indicative of the context orconditions that may have influenced the yield value for the pastyear(s). For example, contextual data 337 can include soil type,elevation, slope, plant date, harvest date, fertilizer application, seedtype (hybrids, etc.), a measure of weed presence, a measure of pestpresence, weather conditions, e.g., rainfall, snow coverage, hail, wind,temperature, etc. Historical yield map 333 can include other items aswell, as indicated by block 339. As shown in the illustrated example,vegetative index map 332 does not contain additional information.However in other examples, vegetative index map 332 can include otheritems as well. For instance, weed growth has an effect on a vegetativeindex reading. Consequently, herbicide application in temporal relationto the vegetative index sensing used to generate vegetative index map332 may be contextual information included in the vegetative index map332 to provide context to the vegetative index values.

Besides receiving one or more of a vegetative index map 332 or ahistorical yield map 333 as a prior information map, predictive modelgenerator 210 also receives a geographic location indicator 334, or anindication of a geographic location, from geographic position sensor204. In-situ sensors 208 illustratively include an on-board yield sensor336 as well as a processing system 338. The processing system 338processes sensor data generated from the on-board yield sensors 336.

In some examples, on-board yield sensor 336 may be an optical sensor onagricultural harvester 100. In some instances, the optical sensor may bea camera or other device that performs optical sensing. The opticalsensor may be arranged in grain tank 132 to collect images of thestorage area of grain tank 132 as agricultural harvester 100 movesthrough the field during a harvesting operation. Processing system 338processes one or more images obtained via the on-board yield sensor 336to generate processed image data identifying one or more characteristicsof grain in the image. Grain characteristics detected by the processingsystem 338 may include one or more of volume, shape, and orientation ofharvested grain in tank 132 over time, which is indicative of theharvested grain yield. Processing system 338 can also geolocate thevalues received from the in-situ sensor 208. For example, the locationof the agricultural harvester at the time a signal from in-situ sensor208 is received is typically not the accurate location of the yield.This is because an amount of time elapses between when the agriculturalharvester makes initial contact with the plant and when the grain fromthe plant is processed by the agricultural harvester or when theprocessed grain is delivered to a storage location on the agriculturalharvester. Thus, a transient time between when a plant is initiallyencountered and when grain from the plant is sensed within theagricultural harvester is taken into account when georeferencing thesensed data. By doing so, an accurate yield measurement can be sensed.Due to travel of severed crop along a header in a direction that istransverse to a direction of travel of the agricultural harvester, theyield values normally geolocate to a chevron shape area rearward of theagricultural harvester as the agricultural harvester travels in aforward direction.

Processing system 338 allocates or apportions an aggregate yielddetected by a yield sensor during each time or measurement interval backto earlier geo-referenced regions based upon the travel times of thecrop from different portions of the agricultural harvester, such asdifferent lateral locations along a width of a header of theagricultural harvester. For example, processing system 338 allocates ameasured aggregate yield from a measurement interval or time back togeo-referenced regions that were traversed by a header of theagricultural harvester during different measurement intervals or times.The processing system 338 apportions or allocates the aggregate yieldfrom a particular measurement interval or time to previously traversedgeo-referenced regions which are part of the chevron shape area.

In other examples, on-board yield sensor 336 can rely on different typesof radiation and the way in which radiation is reflected by, absorbedby, attenuated by, or transmitted through the biomass or the harvestedgrain. The yield sensor 336 may sense other electromagnetic propertiesof grain and biomass such as electrical permittivity when the materialpasses between two capacitive plates. The yield sensor 336 may also relyon mechanical properties of grains and biomass such as a signalgenerated when a grain impacts a piezoelectric sheet or when the impactis detected by a microphone or accelerometer. Other material propertiesand sensors may also be used. In some examples, raw or processed datafrom on-board yield sensor 336 may be presented to operator 260 viaoperator interface mechanism 218. Operator 260 may be onboard of thework agricultural harvester 100 or at a remote location.

The present discussion proceeds with respect to an example in whichon-board yield sensor 336 is an impact plate sensor. It will beappreciated that this is merely one example, and the sensors mentionedabove, as other examples of on-board yield sensor 336, are contemplatedherein as well. As shown in FIG. 4, the predictive model generator 210includes a vegetative index-to-yield model generator 342, and ahistorical yield-to-yield model generator 344. In other examples, thepredictive model generator 210 may include additional, fewer, ordifferent components than those shown in the example of FIG. 4.Consequently, in some examples, the predictive model generator 210 mayinclude other items 348 as well, which may include other types ofpredictive model generators to generate other types of yield models.

Model generator 342 identifies a relationship between in-situ yield data340 at a geographic location corresponding to where in-situ yield data340 was geolocated and vegetative index values from the vegetative indexmap 332 corresponding to the same location in the field where yield data340 was geolocated. Based on this relationship established by modelgenerator 342, model generator 342 generates a predictive yield model.The yield model is used by predictive map generator 212 to predict ayield at different locations in the field based upon the georeferencedvegetative index value contained in the vegetative index map 332 at thesame locations in the field.

Model generator 344 identifies a relationship between the yieldrepresented in the yield data 340, at a geographic locationcorresponding to where the yield data 340 was geolocated, and thehistorical yield at the same location (or a location in a historicalyield map 333 with similar contextual data 337 as the present area oryear). The historical yield value 335 is the georeferenced andcontextually-referenced value contained in the historical yield map 333.Model generator 344 then generates a predictive yield model that is usedby map generator 212 to predict the yield at a location in the fieldbased upon the historical yield value.

In light of the above, the predictive model generator 210 is operable toproduce a plurality of predictive yield models, such as one or more ofthe predictive yield models generated by model generators 342 and 344.In another example, two or more of the predictive yield models describedabove may be combined into a single predictive yield model that predictsa yield based upon the vegetative index value or the historical yield atdifferent locations in the field or both. Any of these yield models, orcombinations thereof, are represented collectively by yield model 350 inFIG. 4.

The predictive yield model 350 is provided to predictive map generator212. In the example of FIG. 4, predictive map generator 212 includes ayield map generator 352. In other examples, the predictive map generator212 may include additional, fewer, or different map generators. Yieldmap generator 352 receives the predictive yield model 350 that predictsyield based upon in-situ data 340 along with one or both of thevegetative index map 332 and historical yield map 333.

Yield map generator 352 can generate a functional predictive yield map360 that predicts yield at different locations in the field based uponthe vegetative index value or historical yield value at those locationsin the field and the predictive yield model 350. The generatedfunctional predictive yield map 360 may be provided to control zonegenerator 213, control system 214, or both. Control zone generator 213generates control zones and incorporates those control zones into thefunctional predictive map 360. Functional predictive map 360 may bepresented to the operator 260 or anther user or be provided to controlsystem 214, which generates control signals to control one or more ofthe controllable subsystems 216 based upon the functional predictive map360.

FIG. 5 is a flow diagram of an example of operation of predictive modelgenerator 210 and predictive map generator 212 in generating thepredictive yield model 350 and the functional predictive yield map 360.At block 362, predictive model generator 210 and predictive mapgenerator 212 receive one or more prior vegetative index maps 332 or oneor more historical yield maps 333 or both. At block 362, a yield sensorsignal is received from an on-board yield sensor 336. As discussedabove, the on-board yield sensor 336 may be an optical sensor 365 ingrain tank 132 or elsewhere, a gamma ray attenuation sensor 366, animpact plate sensor 367, load cells 368, or other yield sensor 370.

At block 363, prior information map selector 209 selects one or morespecific prior information map(s) 250 for use by predictive modelgenerator 210. In one example, prior information map selector 209selects a map from a plurality of candidate maps based on a comparisonof the contextual information in the candidate maps with the currentcontextual information. For example, a candidate historical yield mapmay be selected from a prior year in which weather conditions over thegrowth season were similar to the present year's weather conditions. Or,for example, a candidate historical yield map may be selected from aprior year having a below average level of precipitation, while thepresent year has an average or above average level of precipitation,because the historical yield map associated with a previous year withbelow average precipitation may still have a useful historicalyield-yield relationship, as discussed above. In some examples, priorinformation map selector 209 can change which prior information map isbeing used upon detection that one of the other candidate priorinformation maps is more closely correlating to the in-situ sensedyield.

At block 372, processing system 338 processes the one or more receivedsensor signals received from the on-board yield sensors 336 to generatea yield value indicative of a yield characteristic of the harvestedgrain.

At block 382, predictive model generator 210 also obtains the geographiclocation corresponding to the sensor signal. For instance, thepredictive model generator 210 can obtain the geographic position fromgeographic position sensor 204 and determine, based upon machine delays(e.g., machine processing speed) and machine speed, an accurategeographic location where the in-situ sensed crop yield is to beattributed. For example, the exact time a yield sensor signal iscaptured typically does not correspond to a time when the crop wassevered from the ground. Thus, a position of the agricultural harvester100 when the yield sensor signal is obtained does not correspond to thelocation where the crop was planted. Instead, the current in-situ yieldsensor signal corresponds to a location on the field reward ofagricultural harvester 100 since an amount of time transpires betweenwhen initial contact between the crop and the agricultural harvesteroccurs and when the crop reaches yield sensor 336.

At block 384, predictive model generator 210 generates one or morepredictive yield models, such as yield model 350, that model arelationship between at least one of a vegetative index value orhistorical yield value obtained from a prior information map, such asprior information map 258, and a yield being sensed by the in-situsensor 208. For instance, predictive model generator 210 may generate apredictive yield model based on a historical yield value and a sensedyield indicated by the sensor signal obtained from in-situ sensor 208.

At block 386, the predictive yield model, such as predictive yield model350, is provided to predictive map generator 212 which generates afunctional predictive yield map that maps a predicted yield to differentgeographic locations in the field based on the vegetative index map orhistorical yield map and the predictive yield model 350. For instance,in some examples, the functional predictive yield map 360 predictsyield. In other examples, the functional predictive yield map 360 mappredicts other items, as indicated by block 392. Further, the functionalpredictive yield map 360 can be generated during the course of anagricultural harvesting operation. Thus, as an agricultural harvester ismoving through a field performing an agricultural harvesting operation,the functional predictive yield map 360 is generated.

At block 394, predictive map generator 212 outputs the functionalpredictive yield map 360. At block 393, predictive map generator 212configures the functional predictive yield map 360 for consumption bycontrol system 214. At block 395, predictive map generator 212 can alsoprovide the map 360 to control zone generator 213 for generation ofcontrol zones. At block 397, predictive map generator 212 configures themap 360 in other ways as well. The functional predictive yield map 360(with or without the control zones) is provided to control system 214.At block 396, control system 214 generates control signals to controlthe controllable subsystems 216 based upon the functional predictiveyield map 360.

It can thus be seen that the present system takes a prior informationmap that maps a characteristic such as a vegetative index value orhistorical yield values to different locations in a field. The presentsystem also uses one or more in-situ sensors that sense in-situ sensordata that is indicative of a characteristic, such as yield, andgenerates a model that models a relationship between the yield sensedin-situ using the in-situ sensor and the characteristic mapped in theprior information map. Thus, the present system generates a functionalpredictive map using a model and a prior information map and mayconfigure the generated functional predictive map for consumption by acontrol system or for presentation to a local or remote operator orother user. For example, the control system may use the map to controlone or more systems of a combine harvester.

FIG. 6A is a block diagram of an example portion of the agriculturalharvester 100 shown in FIG. 1. Particularly, FIG. 6A shows, among otherthings, examples of predictive model generator 210 and predictive mapgenerator 212. In the illustrated example, the prior information map 258is one or more of a vegetative index map 332, a historical yield map333, a predictive yield map 360, or a prior operation map 400.

Also, in the example shown in FIG. 6A, in-situ sensor 208 can includeone or more biomass sensors 401, yield sensor 336, an operator inputsensor 407, and a processing system 408. In-situ sensors 208 can includeother sensors 410 as well. Some examples of these other sensors 410 areshown in FIG. 6B.

Biomass sensor 401 senses a variable indicative of the biomass ofmaterial being processed by agricultural harvester 100. In someexamples, biomass sensor 401 can be an optical sensor 402, such as oneof the optical sensors or cameras discussed above. In some examples,biomass sensor 401 can be a rotor pressure sensor 412 or another sensor414. Rotor pressure sensor 412 may sense the rotor drive pressure of athreshing rotor 112. The rotor drive pressure of threshing rotor 112 isindicative of the torque exerted by rotor 112 on the material beingprocessed by agricultural harvester 100. As the biomass of materialbeing processed by agricultural harvester 100 increases, the rotor drivepressure increases as well. Therefore, by sensing the rotor drivepressure, an indication of the biomass of material being processed canbe obtained. Moisture sensor 403 senses a variable indicative of themoisture of material being processed by, or proximate agriculturalharvester 100. In some examples, biomass sensor 401 can include a cropheight sensor 404 or another sensor 405.

Operator input sensor 407 illustratively senses various operator inputs.The inputs can be setting inputs for controlling the settings onagricultural harvester 100 or other control inputs, such as steeringinputs and other inputs. Thus, when operator 260 changes a setting orprovides a commanded input through an operator interface mechanism 218,such an input is detected by operator input sensor 407, which provides asensor signal indicative of that sensed operator input.

Processing system 408 may receive the sensor signals from biomass sensor401 or operator input sensor 407 or both and generate an outputindicative of the sensed variable. For instance, processing system 408may receive a sensor input from optical sensor 402 or rotor pressuresensor 403 and generate an output indicative of biomass. Processingsystem 408 may also receive an input from operator input sensor 407 andgenerate an output indicative of the sensed operator input.

Predictive model generator 210 may include yield-to-biomass modelgenerator 412, historical yield-to-yield model generator 413,yield-to-feed rate model generator 414, yield-to-command model generator415, yield-to-sensor model generator 416, vegetative index-to-biomassmodel generator 417, vegetative index-to-yield model generator 418,vegetative index-to-feed rate model generator 419, vegetativeindex-to-command model generator 420, and vegetative index-to-sensormodel generator 421. In other examples, predictive model generator 210can include additional, fewer, or other model generators 422. Predictivemodel generator 210 may receive a geographic location 334 fromgeographic position sensor 204 (shown in FIG. 2) and generate apredictive model 423 that models a relationship between the informationin one or more of the prior information maps 258 and one or more of: thebiomass sensed by biomass sensor 401; the yield sensed by yield sensor336; and operator input commands sensed by operator input sensor 407.

For instance, yield-to-biomass model generator 412 generates arelationship between a yield as reflected on historical yield map 333,predictive yield map 360, the prior operation map 400, or anycombination thereof and the biomass values sensed by biomass sensor 401.Historical yield-to-yield model generator 413 illustratively generates amodel that represents a relationship between the historical yield (fromhistorical yield map 333) and the yield sensed by yield sensor 336.

Yield-to-feed rate model generator 414 illustratively generates a modelthat represents a relationship between a yield as reflected onhistorical yield map 333, predictive yield map 360, the prior operationmap 400, or any combination thereof and the feed rate or variableindicative of feed rate sensed by an in-situ sensor 208.

Yield-to-operator command model generator 415 generates a model thatmodels the relationship between a yield as reflected on historical yieldmap 333, predictive yield map 360, the prior operation map 400, or anycombination thereof and operator input commands that are sensed byoperator input sensor 407.

Yield-to-sensor model generator 416 generates a relationship between ayield as reflected on historical yield map 333, predictive yield map360, the prior operation map 400, or any combination thereof and thevalues sensed by other sensors 410.

Vegetative index-to-biomass model generator 417 generates a relationshipbetween vegetative index values from vegetative index map 332 and thebiomass values sensed by biomass sensor 401.

Vegetative index-to-yield model generator 418 illustratively generates amodel that represents a relationship between the vegetative index valuefrom vegetative index map 332 and the yield sensed by yield sensor 336.

Vegetative index-to-feed rate model generator 419 illustrativelygenerates a model that represents a relationship between the vegetativeindex values from vegetative index map 332 and the feed rate sensed byan in-situ sensor 208.

Vegetative index-to-operator command model generator 420 generates amodel that models the relationship between a vegetative index asreflected on vegetative index map 332 and operator input commands thatare sensed by operator input sensor 407.

Vegetative index-to-sensor model generator 421 generates a relationshipbetween vegetative index values from vegetative index map 332 and thevalues sensed by other sensors 410.

Predictive model 423 generated by the predictive model generator 210 caninclude one or more of the predictive models that may be generated byyield-to-biomass model generator 412, historical yield-to-yield modelgenerator 413, yield-to-feed rate model generator 414, yield-to-commandmodel generator 415, yield-to-sensor model generator 416, vegetativeindex-to-biomass model generator 417, vegetative index-to-yield modelgenerator 418, vegetative index-to-feed rate model generator 419,vegetative index-to-command model generator 420, and vegetativeindex-to-sensor model generator 421, and other model generators that maybe included as part of other items 422.

In the example of FIG. 6, predictive map generator 212 includespredictive biomass map generator 424, predictive yield map generator425, predictive feed rate map generator 426, predictive sensor data mapgenerator 427, and a predictive operator command map generator 428. Inother examples, predictive map generator 212 can include additional,fewer, or other map generators 429.

Predictive biomass map generator 424 receives a predictive model 423that models the relationship between a yield or vegetative index andbiomass (such as a predictive model generated by yield-to-biomass modelgenerator 412 or vegetative index-to-biomass model generator 417), andone or more of the prior information maps 258. Predictive biomass mapgenerator 424 generates a functional predictive biomass map 430 thatpredicts biomass at different locations in the field based upon one ormore of the yield and vegetative index in one or more of the priorinformation maps 258 at those locations in the field and based onpredictive model 423.

Predictive yield map generator 425 receives a predictive model 423 thatmodels the relationship between a historical yield or vegetative indexand yield (such as a predictive model generated by historicalyield-to-yield model generator 413 or vegetative index-to-yield modelgenerator 418), and one or more of the prior information maps 258.Predictive yield map generator 425 generates a functional predictiveyield map 431 that predicts yield at different locations in the fieldbased upon one or more of the historical yield and vegetative index inone or more of the prior information maps 258 at those locations in thefield and based on predictive model 423.

Predictive feed rate map generator 426 receives a predictive model 423that models the relationship between a yield or vegetative index andfeed rate (such as a predictive model generated by yield-to-feed ratemodel generator 414 or vegetative index-to-feed rate model generator419), and one or more of the prior information maps 258. Predictive feedrate map generator 426 generates a functional predictive feed rate map432 that predicts desirable feed rates at different locations in thefield based upon one or more of the yield and vegetative index in one ormore of the prior information maps 258 at those locations in the fieldand based on predictive model 423.

Predictive operator command map generator 427 receives a predictivemodel 423 (such as a predictive model generated by yield-to-commandmodel generator 415 or vegetative index-to-command model generator 420),that models the relationship between the yield or vegetative index andoperator command inputs detected by operator input sensor 407 andgenerates a functional predictive operator command map 434 that predictsoperator command inputs at different locations in the field based uponone or more of the yield and vegetative index in one or more of theprior information maps 258 at those locations in the field and based onpredictive model 423.

Predictive sensor data map generator 427 receives a predictive model 423that models the relationship between a yield or vegetative index andsensor data (such as a predictive model generated by yield-to-sensordata model generator 413 or vegetative index-to-sensor model generator421), and one or more of the prior information maps 258. Predictiveyield map generator 425 generates a functional predictive sensor datamap 433 that predicts sensor data at different locations in the fieldbased upon one or more of the yield and vegetative index values in oneor more of the prior information maps 258 at those locations in thefield and based on predictive model 423.

Predictive map generator 212 outputs one or more of the functionalpredictive maps 430, 431, 432, 433, and 434. Each of the functionalpredictive maps 430, 431, 432, 433, and 434 may be provided to controlzone generator 213, control system 214, or both. Control zone generator213 generates control zones and incorporates those control zones intothe functional predictive maps 430, 431, 432, 433, and 434. Any or allof functional predictive maps 430, 431, 432, 433, or 434 and thecorresponding functional predictive maps 430, 431, 432, 433, or 434having control zones may be provided to control system 214, whichgenerates control signals to control one or more of the controllablesubsystems 216 based upon one or all of the functional predictive maps.Any or all of the maps 430, 431, 432, 433, or 434 (with or withoutcontrol zones) may be presented to operator 260 or another user.

FIG. 6B is a block diagram showing some examples of real-time (in-situ)sensors 208. Some of the sensors shown in FIG. 6B, or differentcombinations of them, may have both a sensor 336 and a processing system338. Some of the possible in-situ sensors 208 shown in FIG. 6B are shownand described above with respect to previous FIGS. and are similarlynumbered. FIG. 6B shows that in-situ sensors 208 can include operatorinput sensors 980, machine sensors 982, harvested material propertysensors 984, field and soil property sensors 985, environmentalcharacteristic sensors 987, and they may include a wide variety of othersensors 226. Operator input sensors 980 may be sensors that senseoperator inputs through operator interface mechanisms 218. Therefore,operator input sensors 980 may sense user movement of linkages,joysticks, a steering wheel, buttons, dials, or pedals. Operator inputsensors 980 can also sense user interactions with other operator inputmechanisms, such as with a touch sensitive screen, with a microphonewhere speech recognition is utilized, or any of a wide variety of otheroperator input mechanisms.

Machine sensors 982 may sense different characteristics of agriculturalharvester 100. For instance, as discussed above, machine sensors 982 mayinclude machine speed sensors 146, separator loss sensor 148, cleangrain camera 150, forward looking image capture mechanism 151, losssensors 152 or geographic position sensor 204, examples of which aredescribed above. Machine sensors 982 can also include machine settingsensors 991 that sense machine settings. Some examples of machinesettings were described above with respect to FIG. 1. Front-endequipment (e.g., header) position sensor 993 can sense the position ofthe header 102, reel 164, cutter 104, or other front-end equipmentrelative to the frame of agricultural harvester 100. For instance,sensors 993 may sense the height of header 102 above the ground. Machinesensors 982 can also include front-end equipment (e.g., header)orientation sensors 995. Sensors 995 may sense the orientation of header102 relative to agricultural harvester 100, or relative to the ground.Machine sensors 982 may include stability sensors 997. Stability sensors997 sense oscillation or bouncing motion (and amplitude) of agriculturalharvester 100. Machine sensors 982 may also include residue settingsensors 999 that are configured to sense whether agricultural harvester100 is configured to chop the residue, produce a windrow, or deal withthe residue in another way. Machine sensors 982 may include cleaningshoe fan speed sensor 951 that senses the speed of cleaning fan 120.Machine sensors 982 may include concave clearance sensors 953 that sensethe clearance between the rotor 112 and concaves 114 on agriculturalharvester 100. Machine sensors 982 may include chaffer clearance sensors955 that sense the size of openings in chaffer 122. The machine sensors982 may include threshing rotor speed sensor 957 that senses a rotorspeed of rotor 112. Machine sensors 982 may include rotor pressuresensor 959 that senses the pressure used to drive rotor 112. Machinesensors 982 may include sieve clearance sensor 961 that senses the sizeof openings in sieve 124. The machine sensors 982 may include MOGmoisture sensor 963 that senses a moisture level of the MOG passingthrough agricultural harvester 100. Machine sensors 982 may includemachine orientation sensor 965 that senses the orientation ofagricultural harvester 100. Machine sensors 982 may include materialfeed rate sensors 967 that sense the feed rate of material as thematerial travels through feeder house 106, clean grain elevator 130, orelsewhere in agricultural harvester 100. Machine sensors 982 can includebiomass sensors 969 that sense the biomass traveling through feederhouse 106, through separator 116, or elsewhere in agricultural harvester100. The machine sensors 982 may include fuel consumption sensor 971that senses a rate of fuel consumption over time of agriculturalharvester 100. Machine sensors 982 may include power utilization sensor973 that senses power utilization in agricultural harvester 100, such aswhich subsystems are utilizing power, or the rate at which subsystemsare utilizing power, or the distribution of power among the subsystemsin agricultural harvester 100. Machine sensors 982 may include tirepressure sensors 977 that sense the inflation pressure in tires 144 ofagricultural harvester 100. Machine sensor 982 may include a widevariety of other machine performance sensors, or machine characteristicsensors, indicated by block 975. The machine performance sensors andmachine characteristic sensors 975 may sense machine performance orcharacteristics of agricultural harvester 100.

Harvested material property sensors 984 may sense characteristics of thesevered crop material as the crop material is being processed byagricultural harvester 100. The crop properties may include such thingsas crop type, crop moisture, grain quality (such as broken grain), MOGlevels, grain constituents such as starches and protein, MOG moisture,and other crop material properties. Other sensors could sense straw“toughness”, adhesion of corn to ears, and other characteristics thatmight be beneficially used to control processing for better graincapture, reduced grain damage, reduced power consumption, reduced grainloss, etc.

Field and soil property sensors 985 may sense characteristics of thefield and soil. The field and soil properties may include soil moisture,soil compactness, the presence and location of standing water, soiltype, and other soil and field characteristics.

Environmental characteristic sensors 987 may sense one or moreenvironmental characteristics. The environmental characteristics mayinclude such things as wind direction and wind speed, precipitation,fog, dust level or other obscurants, or other environmentalcharacteristics. FIG. 7 shows a flow diagram illustrating one example ofthe operation of predictive model generator 210 and predictive mapgenerator 212 in generating one or more predictive models 423 and one ormore functional predictive maps 430, 431, 432, 433, and 434. At block442, predictive model generator 210 and predictive map generator 212receive a prior information map 258. The prior information map 258 maybe vegetative index map 332, predictive yield map 360, a historicalyield map 333, or a prior operation map 400 created using data obtainedduring a prior operation in a field. At block 444, predictive modelgenerator 210 receives a sensor signal containing sensor data from anin-situ sensor 208. The in-situ sensor can be one or more of a biomasssensor 401, which may be an optical sensor 402 or a rotor pressuresensor 403, a yield sensor 336, an operator input sensor 407, or othersensors 410, shown in FIG. 6A. Some examples of other sensors 410 areshown in FIG. 6B.

At block 454, processing system 408 processes the data contained in thesensor signal or signals received from the in-situ sensor or sensors 208to obtain processed data 411, shown in FIG. 6. The data contained in thesensor signal or signals can be in a raw format that is processed toproduce processed data 411. For example, a temperature sensor signalincludes electrical resistance data. This electrical resistance data canbe processed into temperature data. In other examples, processing maycomprise digitizing, encoding, formatting, scaling, filtering, orclassifying data. The processed data 411 may be indicative of one ormore of biomass, yield, feed rate, an operator input command, or anothersensed characteristic. The processed data 411 is provided to predictivemodel generator 210.

Returning to FIG. 7, at block 456, predictive model generator 210 alsoreceives a geographic location 334 from geographic position sensor 204,as shown in FIG. 6. The geographic location 334 may be correlated to thegeographic location from which the sensed variable or variables, sensedby in-situ sensors 208, were taken. For instance, the predictive modelgenerator 210 can obtain the geographic location 334 from geographicposition sensor 204 and determine, based upon machine delays, machinespeed, etc., a precise geographic location from which the processed data411 was derived.

At block 458, predictive model generator 210 generates one or morepredictive models 423 that model a relationship between a mapped valuein a prior information map and a characteristic represented in theprocessed data 411. For example, in some instances, the mapped value ina prior information map may be a yield or vegetative index, which may beone or more of a vegetative index value in vegetative index map 332; apredictive yield value in functional predictive yield map 360; ahistorical yield in historical yield map 333; or a different value inprior operation map 400, and the predictive model generator 210generates a predictive model using the mapped value of a priorinformation map and a characteristic sensed by in-situ sensors 208, asrepresented in the processed data 411, or a related characteristic, suchas a characteristic that correlates to the characteristic sensed byin-situ sensors 208.

The one or more predictive models 423 are provided to predictive mapgenerator 212. At block 466, predictive map generator 212 generates oneor more functional predictive maps 264. The functional predictive maps264 may be functional predictive biomass map 430, functional predictiveyield map 431, functional predictive feed rate map 432, functionalpredictive sensor data map 433, functional predictive operator commandmap 434, or any combination of these maps. Functional predictive biomassmap 430 predicts a biomass that will be encountered by agriculturalharvester 100 at different locations in the field. Functional predictiveyield map 431 predicts a yield that will be encountered by agriculturalharvester 100 at different locations in the field. Functional predictivefeed rate map 432 predicts a desired feed rate for agriculturalharvester 100 at different locations in the field. Functional predictivesensor data map 433 predicts a sensor data value that is expected to bedetected by one or more in-situ sensors 208 at different locations inthe field. Functional predictive operator command map 434 predictslikely operator command inputs at different locations in the field.Further, one or more of the functional predictive maps 430, 431, 432,433, and 434 can be generated during the course of an agriculturaloperation. Thus, as agricultural harvester 100 is moving through a fieldperforming an agricultural operation, the one or more predictive maps430, 431, 432, 433, and 434 are generated as the agricultural operationis being performed.

At block 468, predictive map generator 212 outputs the one or morefunctional predictive maps 430, 431, 432, 433, and 434. At block 470,predictive map generator 212 may configure the map for presentation toand possible interaction by an operator 260 or another user. At block472, predictive map generator 212 may configure the map for consumptionby control system 214. At block 474, predictive map generator 212 canprovide the one or more predictive maps 430, 431, 432, 433, and 434 tocontrol zone generator 213 for generation of control zones. At block476, predictive map generator 212 configures the one or predictive maps430, 431, 432, 433, and 434 in other ways. In an example in which theone or more functional predictive maps 430, 431, 432, 433, and 434 areprovided to control zone generator 213, the one or more functionalpredictive maps 430, 431, 432, 433, and 434, with the control zonesincluded therewith, represented by corresponding maps 265, describedabove, may be presented to operator 260 or another user or provided tocontrol system 214 as well.

At block 478, control system 214 then generates control signals tocontrol the controllable subsystems based upon the one or morefunctional predictive maps 430, 431, 432, 433, and 434 (or thefunctional predictive maps 430, 431, 432, 433, and 434 having controlzones) as well as an input from the geographic position sensor 204. Forexample, when the functional predictive feed rate map 432 or functionalpredictive feed rate map 432 containing control zones is provided tocontrol system 214, feed rate controller 236, in response, generatescontrol signals to control the controllable subsystems 216 in order tocontrol the feed rate of material through agricultural harvester 100based upon the predicted feed rate values.

In another example, the header and reel controller 238 controls a heightof header 102 based upon the predictive biomass values in functionalpredictive biomass map 430 or functional predictive machine biomass map430 containing control zones. This can be used to maintain a desiredfeed rate of material through agricultural harvester 100 or maintain adesired stubble height to capture snow for improved soil moisture incoming year.

In another example, settings controller 232 generates control signals tocontrol the controllable subsystems 216 to automatically generatecommand inputs or to recommend command inputs to operator 260 based uponthe operator command values in functional predictive operator commandmap 434 or functional predictive operator command map 434 containingcontrol zones. Agricultural harvester 100 could be controlled in otherways as well with other generated control signals, some of which aredescribed below.

In another example in which control system 214 receives the functionalpredictive sensor data map 433 or the functional predictive sensor datamap 433 with control zones added, the path planning controller 234controls steering subsystem 252 to steer agricultural harvester 100. Inanother example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the residue system controller 244controls residue subsystem 138. In another example in which controlsystem 214 receives the functional predictive sensor data map 436 or thefunctional predictive sensor data map 436 with control zones added, thesettings controller 232 controls thresher settings of thresher 110. Inanother example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the settings controller 232 or anothercontroller 246 controls material handling subsystem 125. In anotherexample in which control system 214 receives the functional predictivesensor data map 436 or the functional predictive sensor data map 436with control zones added, the settings controller 232 controls machinecleaning subsystem 254. In another example in which control system 214receives the functional predictive sensor data map 436 or the functionalpredictive sensor data map 436 with control zones added, the machinecleaning controller 245 controls machine cleaning subsystem 254 onagricultural harvester 100. In another example in which control system214 receives the functional predictive sensor data map 436 or thefunctional predictive sensor data map 436 with control zones added, thecommunication system controller 229 controls communication system 206.In another example in which control system 214 receives the functionalpredictive sensor data map 436 or the functional predictive sensor datamap 436 with control zones added, the operator interface controller 231controls operator interface mechanisms 218 on agricultural harvester100. In another example in which control system 214 receives thefunctional predictive sensor data map 436 or the functional predictivesensor data map 436 with control zones added, the deck plate positioncontroller 242 controls machine/header actuators to control a deck plateon agricultural harvester 100. In another example in which controlsystem 214 receives the functional predictive sensor data map 436 or thefunctional predictive sensor data map 436 with control zones added, thedraper belt controller 240 controls machine/header actuators to controla draper belt on agricultural harvester 100. In another example in whichcontrol system 214 receives the functional predictive sensor data map436 or the functional predictive sensor data map 436 with control zonesadded, the other controllers 246 control other controllable subsystems256 on agricultural harvester 100.

FIG. 8 shows a block diagram illustrating one example of control zonegenerator 213. Control zone generator 213 includes work machine actuator(WMA) selector 486, control zone generation system 488, and regime zonegeneration system 490. Control zone generator 213 may also include otheritems 492. Control zone generation system 488 includes control zonecriteria identifier component 494, control zone boundary definitioncomponent 496, target setting identifier component 498, and other items520. Regime zone generation system 490 includes regime zone criteriaidentification component 522, regime zone boundary definition component524, settings resolver identifier component 526, and other items 528.Before describing the overall operation of control zone generator 213 inmore detail, a brief description of some of the items in control zonegenerator 213 and the respective operations thereof will first beprovided.

Agricultural harvester 100, or other work machines, may have a widevariety of different types of controllable actuators that performdifferent functions. The controllable actuators on agriculturalharvester 100 or other work machines are collectively referred to aswork machine actuators (WMAs). Each WMA may be independentlycontrollable based upon values on a functional predictive map, or theWMAs may be controlled as sets based upon one or more values on afunctional predictive map. Therefore, control zone generator 213 maygenerate control zones corresponding to each individually controllableWMA or corresponding to the sets of WMAs that are controlled incoordination with one another.

WMA selector 486 selects a WMA or a set of WMAs for which correspondingcontrol zones are to be generated. Control zone generation system 488then generates the control zones for the selected WMA or set of WMAs.For each WMA or set of WMAs, different criteria may be used inidentifying control zones. For example, for one WMA, the WMA responsetime may be used as the criteria for defining the boundaries of thecontrol zones. In another example, wear characteristics (e.g., how mucha particular actuator or mechanism wears as a result of movementthereof) may be used as the criteria for identifying the boundaries ofcontrol zones. Control zone criteria identifier component 494 identifiesparticular criteria that are to be used in defining control zones forthe selected WMA or set of WMAs. Control zone boundary definitioncomponent 496 processes the values on a functional predictive map underanalysis to define the boundaries of the control zones on thatfunctional predictive map based upon the values in the functionalpredictive map under analysis and based upon the control zone criteriafor the selected WMA or set of WMAs.

Target setting identifier component 498 sets a value of the targetsetting that will be used to control the WMA or set of WMAs in differentcontrol zones. For instance, if the selected WMA is propulsion system250 and the functional predictive map under analysis is a functionalpredictive speed map 438, then the target setting in each control zonemay be a target speed setting based on speed values contained in thefunctional predictive speed map 238 within the identified control zone.

In some examples, where agricultural harvester 100 is to be controlledbased on a current or future location of the agricultural harvester 100,multiple target settings may be possible for a WMA at a given position.In that case, the target settings may have different values and may becompeting. Thus, the target settings need to be resolved so that only asingle target setting is used to control the WMA. For example, where theWMA is an actuator in propulsion system 250 that is being controlled inorder to control the speed of agricultural harvester 100, multipledifferent competing sets of criteria may exist that are considered bycontrol zone generation system 488 in identifying the control zones andthe target settings for the selected WMA in the control zones. Forinstance, different target settings for controlling machine speed may begenerated based upon, for example, a detected or predicted feed ratevalue, a detected or predictive fuel efficiency value, a detected orpredicted grain loss value, or a combination of these. However, at anygiven time, the agricultural harvester 100 cannot travel over the groundat multiple speeds simultaneously. Rather, at any given time, theagricultural harvester 100 travels at a single speed. Thus, one of thecompeting target settings is selected to control the speed ofagricultural harvester 100.

Therefore, in some examples, regime zone generation system 490 generatesregime zones to resolve multiple different competing target settings.Regime zone criteria identification component 522 identifies thecriteria that are used to establish regime zones for the selected WMA orset of WMAs on the functional predictive map under analysis. Somecriteria that can be used to identify or define regime zones include,for example, crop type or crop variety based on an as-planted map oranother source of the crop type or crop variety, weed type, weedintensity, or crop state, such as whether the crop is down, partiallydown or standing. Just as each WMA or set of WMAs may have acorresponding control zone, different WMAs or sets of WMAs may have acorresponding regime zone. Regime zone boundary definition component 524identifies the boundaries of regime zones on the functional predictivemap under analysis based on the regime zone criteria identified byregime zone criteria identification component 522.

In some examples, regime zones may overlap with one another. Forinstance, a crop variety regime zone may overlap with a portion of or anentirety of a crop state regime zone. In such an example, the differentregime zones may be assigned to a precedence hierarchy so that, wheretwo or more regime zones overlap, the regime zone assigned with agreater hierarchical position or importance in the precedence hierarchyhas precedence over the regime zones that have lesser hierarchicalpositions or importance in the precedence hierarchy. The precedencehierarchy of the regime zones may be manually set or may beautomatically set using a rules-based system, a model-based system, oranother system. As one example, where a downed crop regime zone overlapswith a crop variety regime zone, the downed crop regime zone may beassigned a greater importance in the precedence hierarchy than the cropvariety regime zone so that the downed crop regime zone takesprecedence.

In addition, each regime zone may have a unique settings resolver for agiven WMA or set of WMAs. Settings resolver identifier component 526identifies a particular settings resolver for each regime zoneidentified on the functional predictive map under analysis and aparticular settings resolver for the selected WMA or set of WMAs.

Once the settings resolver for a particular regime zone is identified,that settings resolver may be used to resolve competing target settings,where more than one target setting is identified based upon the controlzones. The different types of settings resolvers can have differentforms. For instance, the settings resolvers that are identified for eachregime zone may include a human choice resolver in which the competingtarget settings are presented to an operator or other user forresolution. In another example, the settings resolver may include aneural network or other artificial intelligence or machine learningsystem. In such instances, the settings resolvers may resolve thecompeting target settings based upon a predicted or historic qualitymetric corresponding to each of the different target settings. As anexample, an increased vehicle speed setting may reduce the time toharvest a field and reduce corresponding time-based labor and equipmentcosts but may increase grain losses. A reduced vehicle speed setting mayincrease the time to harvest a field and increase correspondingtime-based labor and equipment costs but may decrease grain losses. Whengrain loss or time to harvest is selected as a quality metric, thepredicted or historic value for the selected quality metric, given thetwo competing vehicle speed settings values, may be used to resolve thespeed setting. In some instances, the settings resolvers may be a set ofthreshold rules that may be used instead of, or in addition to, theregime zones. An example of a threshold rule may be expressed asfollows:

-   -   If predicted biomass values within 20 feet of the header of the        agricultural harvester 100 are greater that x kilograms (where x        is a selected or predetermined value), then use the target        setting value that is chosen based on feed rate over other        competing target settings, otherwise use the target setting        value based on grain loss over other competing target setting        values.

The settings resolvers may be logical components that execute logicalrules in identifying a target setting. For instance, the settingsresolver may resolve target settings while attempting to minimizeharvest time or minimize the total harvest cost or maximize harvestedgrain or based on other variables that are computed as a function of thedifferent candidate target settings. A harvest time may be minimizedwhen an amount to complete a harvest is reduced to at or below aselected threshold. A total harvest cost may be minimized where thetotal harvest cost is reduced to at or below a selected threshold.Harvested grain may be maximized where the amount of harvested grain isincreased to at or above a selected threshold.

FIG. 9 is a flow diagram illustrating one example of the operation ofcontrol zone generator 213 in generating control zones and regime zonesfor a map that the control zone generator 213 receives for zoneprocessing (e.g., for a map under analysis).

At block 530, control zone generator 213 receives a map under analysisfor processing. In one example, as shown at block 532, the map underanalysis is a functional predictive map. For example, the map underanalysis may be one of the functional predictive maps 436, 437, 438, or440. Block 534 indicates that the map under analysis can be other mapsas well.

At block 536, WMA selector 486 selects a WMA or a set of WMAs for whichcontrol zones are to be generated on the map under analysis. At block538, control zone criteria identification component 494 obtains controlzone definition criteria for the selected WMAs or set of WMAs. Block 540indicates an example in which the control zone criteria are or includewear characteristics of the selected WMA or set of WMAs. Block 542indicates an example in which the control zone definition criteria areor include a magnitude and variation of input source data, such as themagnitude and variation of the values on the map under analysis or themagnitude and variation of inputs from various in-situ sensors 208.Block 544 indicates an example in which the control zone definitioncriteria are or include physical machine characteristics, such as thephysical dimensions of the machine, a speed at which differentsubsystems operate, or other physical machine characteristics. Block 546indicates an example in which the control zone definition criteria areor include a responsiveness of the selected WMA or set of WMAs inreaching newly commanded setting values. Block 548 indicates an examplein which the control zone definition criteria are or include machineperformance metrics. Block 550 indicates an example in which the controlzone definition criteria are or includes operator preferences. Block 552indicates an example in which the control zone definition criteria areor include other items as well. Block 549 indicates an example in whichthe control zone definition criteria are time based, meaning thatagricultural harvester 100 will not cross the boundary of a control zoneuntil a selected amount of time has elapsed since agricultural harvester100 entered a particular control zone. In some instances, the selectedamount of time may be a minimum amount of time. Thus, in some instances,the control zone definition criteria may prevent the agriculturalharvester 100 from crossing a boundary of a control zone until at leastthe selected amount of time has elapsed. Block 551 indicates an examplein which the control zone definition criteria are based on a selectedsize value. For example, a control zone definition criteria that isbased on a selected size value may preclude definition of a control zonethat is smaller than the selected size. In some instances, the selectedsize may be a minimum size.

At block 554, regime zone criteria identification component 522 obtainsregime zone definition criteria for the selected WMA or set of WMAs.Block 556 indicates an example in which the regime zone definitioncriteria are based on a manual input from operator 260 or another user.Block 558 illustrates an example in which the regime zone definitioncriteria are based on crop type or crop variety. Block 560 illustratesan example in which the regime zone definition criteria are based onweed type or weed intensity or both. Block 562 illustrates an example inwhich the regime zone definition criteria are based on or include cropstate. Block 564 indicates an example in which the regime zonedefinition criteria are or include other criteria as well.

At block 566, control zone boundary definition component 496 generatesthe boundaries of control zones on the map under analysis based upon thecontrol zone criteria. Regime zone boundary definition component 524generates the boundaries of regime zones on the map under analysis basedupon the regime zone criteria. Block 568 indicates an example in whichthe zone boundaries are identified for the control zones and the regimezones. Block 570 shows that target setting identifier component 498identifies the target settings for each of the control zones. Thecontrol zones and regime zones can be generated in other ways as well,and this is indicated by block 572.

At block 574, settings resolver identifier component 526 identifies thesettings resolver for the selected WMAs in each regime zone defined byregimes zone boundary definition component 524. As discussed above, theregime zone resolver can be a human resolver 576, an artificialintelligence or machine learning system resolver 578, a resolver 580based on predicted or historic quality for each competing targetsetting, a rules-based resolver 582, a performance criteria-basedresolver 584, or other resolvers 586.

At block 588, WMA selector 486 determines whether there are more WMAs orsets of WMAs to process. If additional WMAs or sets of WMAs areremaining to be processed, processing reverts to block 436 where thenext WMA or set of WMAs for which control zones and regime zones are tobe defined is selected. When no additional WMAs or sets of WMAs forwhich control zones or regime zones are to be generated are remaining,processing moves to block 590 where control zone generator 213 outputs amap with control zones, target settings, regime zones, and settingsresolvers for each of the WMAs or sets of WMAs. As discussed above, theoutputted map can be presented to operator 260 or another user; theoutputted map can be provided to control system 214; or the outputtedmap can be output in other ways.

FIG. 10 illustrates one example of the operation of control system 214in controlling agricultural harvester 100 based upon a map that isoutput by control zone generator 213. Thus, at block 592, control system214 receives a map of the worksite. In some instances, the map can be afunctional predictive map that may include control zones and regimezones, as represented by block 594. In some instances, the received mapmay be a functional predictive map that excludes control zones andregime zones. Block 596 indicates an example in which the received mapof the worksite can be a prior information map having control zones andregime zones identified on it. Block 598 indicates an example in whichthe received map can include multiple different maps or multipledifferent map layers. Block 610 indicates an example in which thereceived map can take other forms as well.

At block 612, control system 214 receives a sensor signal fromgeographic position sensor 204. The sensor signal from geographicposition sensor 204 can include data that indicates the geographiclocation 614 of agricultural harvester 100, the speed 616 ofagricultural harvester 100, the heading 618 or agricultural harvester100, or other information 620. At block 622, zone controller 247 selectsa regime zone, and, at block 624, zone controller 247 selects a controlzone on the map based on the geographic position sensor signal. At block626, zone controller 247 selects a WMA or a set of WMAs to becontrolled. At block 628, zone controller 247 obtains one or more targetsettings for the selected WMA or set of WMAs. The target settings thatare obtained for the selected WMA or set of WMAs may come from a varietyof different sources. For instance, block 630 shows an example in whichone or more of the target settings for the selected WMA or set of WMAsis based on an input from the control zones on the map of the worksite.Block 632 shows an example in which one or more of the target settingsis obtained from human inputs from operator 260 or another user. Block634 shows an example in which the target settings are obtained from anin-situ sensor 208. Block 636 shows an example in which the one or moretarget settings is obtained from one or more sensors on other machinesworking in the same field either concurrently with agriculturalharvester 100 or from one or more sensors on machines that worked in thesame field in the past. Block 638 shows an example in which the targetsettings are obtained from other sources as well.

At block 640, zone controller 247 accesses the settings resolver for theselected regime zone and controls the settings resolver to resolvecompeting target settings into a resolved target setting. As discussedabove, in some instances, the settings resolver may be a human resolverin which case zone controller 247 controls operator interface mechanisms218 to present the competing target settings to operator 260 or anotheruser for resolution. In some instances, the settings resolver may be aneural network or other artificial intelligence or machine learningsystem, and zone controller 247 submits the competing target settings tothe neural network, artificial intelligence, or machine learning systemfor selection. In some instances, the settings resolver may be based ona predicted or historic quality metric, on threshold rules, or onlogical components. In any of these latter examples, zone controller 247executes the settings resolver to obtain a resolved target setting basedon the predicted or historic quality metric, based on the thresholdrules, or with the use of the logical components.

At block 642, with zone controller 247 having identified the resolvedtarget setting, zone controller 247 provides the resolved target settingto other controllers in control system 214, which generate and applycontrol signals to the selected WMA or set of WMAs based upon theresolved target setting. For instance, where the selected WMA is amachine or header actuator 248, zone controller 247 provides theresolved target setting to settings controller 232 or header/realcontroller 238 or both to generate control signals based upon theresolved target setting, and those generated control signals are appliedto the machine or header actuators 248. At block 644, if additional WMAsor additional sets of WMAs are to be controlled at the currentgeographic location of the agricultural harvester 100 (as detected atblock 612), then processing reverts to block 626 where the next WMA orset of WMAs is selected. The processes represented by blocks 626 through644 continue until all of the WMAs or sets of WMAs to be controlled atthe current geographical location of the agricultural harvester 100 havebeen addressed. If no additional WMAs or sets of WMAs are to becontrolled at the current geographic location of the agriculturalharvester 100 remain, processing proceeds to block 646 where zonecontroller 247 determines whether additional control zones to beconsidered exist in the selected regime zone. If additional controlzones to be considered exist, processing reverts to block 624 where anext control zone is selected. If no additional control zones areremaining to be considered, processing proceeds to block 648 where adetermination as to whether additional regime zones are remaining to beconsider. Zone controller 247 determines whether additional regime zonesare remaining to be considered. If additional regimes zone are remainingto be considered, processing reverts to block 622 where a next regimezone is selected.

At block 650, zone controller 247 determines whether the operation thatagricultural harvester 100 is performing is complete. If not, the zonecontroller 247 determines whether a control zone criterion has beensatisfied to continue processing, as indicated by block 652. Forinstance, as mentioned above, control zone definition criteria mayinclude criteria defining when a control zone boundary may be crossed bythe agricultural harvester 100. For example, whether a control zoneboundary may be crossed by the agricultural harvester 100 may be definedby a selected time period, meaning that agricultural harvester 100 isprevented from crossing a zone boundary until a selected amount of timehas transpired. In that case, at block 652, zone controller 247determines whether the selected time period has elapsed. Additionally,zone controller 247 can perform processing continually. Thus, zonecontroller 247 does not wait for any particular time period beforecontinuing to determine whether an operation of the agriculturalharvester 100 is completed. At block 652, zone controller 247 determinesthat it is time to continue processing, then processing continues atblock 612 where zone controller 247 again receives an input fromgeographic position sensor 204. It will also be appreciated that zonecontroller 247 can control the WMAs and sets of WMAs simultaneouslyusing a multiple-input, multiple-output controller instead ofcontrolling the WMAs and sets of WMAs sequentially.

FIG. 11 is a block diagram showing one example of an operator interfacecontroller 231. In an illustrated example, operator interface controller231 includes operator input command processing system 654, othercontroller interaction system 656, speech processing system 658, andaction signal generator 660. Operator input command processing system654 includes speech handling system 662, touch gesture handling system664, and other items 666. Other controller interaction system 656includes controller input processing system 668 and controller outputgenerator 670. Speech processing system 658 includes trigger detector672, recognition component 674, synthesis component 676, naturallanguage understanding system 678, dialog management system 680, andother items 682. Action signal generator 660 includes visual controlsignal generator 684, audio control signal generator 686, haptic controlsignal generator 688, and other items 690. Before describing operationof the example operator interface controller 231 shown in FIG. 11 inhandling various operator interface actions, a brief description of someof the items in operator interface controller 231 and the associatedoperation thereof is first provided.

Operator input command processing system 654 detects operator inputs onoperator interface mechanisms 218 and processes those inputs forcommands. Speech handling system 662 detects speech inputs and handlesthe interactions with speech processing system 658 to process the speechinputs for commands. Touch gesture handling system 664 detects touchgestures on touch sensitive elements in operator interface mechanisms218 and processes those inputs for commands.

Other controller interaction system 656 handles interactions with othercontrollers in control system 214. Controller input processing system668 detects and processes inputs from other controllers in controlsystem 214, and controller output generator 670 generates outputs andprovides those outputs to other controllers in control system 214.Speech processing system 658 recognizes speech inputs, determines themeaning of those inputs, and provides an output indicative of themeaning of the spoken inputs. For instance, speech processing system 658may recognize a speech input from operator 260 as a settings changecommand in which operator 260 is commanding control system 214 to changea setting for a controllable subsystem 216. In such an example, speechprocessing system 658 recognizes the content of the spoken command,identifies the meaning of that command as a settings change command, andprovides the meaning of that input back to speech handling system 662.Speech handling system 662, in turn, interacts with controller outputgenerator 670 to provide the commanded output to the appropriatecontroller in control system 214 to accomplish the spoken settingschange command.

Speech processing system 658 may be invoked in a variety of differentways. For instance, in one example, speech handling system 662continuously provides an input from a microphone (being one of theoperator interface mechanisms 218) to speech processing system 658. Themicrophone detects speech from operator 260, and the speech handlingsystem 662 provides the detected speech to speech processing system 658.Trigger detector 672 detects a trigger indicating that speech processingsystem 658 is invoked. In some instances, when speech processing system658 is receiving continuous speech inputs from speech handling system662, speech recognition component 674 performs continuous speechrecognition on all speech spoken by operator 260. In some instances,speech processing system 658 is configured for invocation using a wakeupword. That is, in some instances, operation of speech processing system658 may be initiated based on recognition of a selected spoken word,referred to as the wakeup word. In such an example, where recognitioncomponent 674 recognizes the wakeup word, the recognition component 674provides an indication that the wakeup word has been recognized totrigger detector 672. Trigger detector 672 detects that speechprocessing system 658 has been invoked or triggered by the wakeup word.In another example, speech processing system 658 may be invoked by anoperator 260 actuating an actuator on a user interface mechanism, suchas by touching an actuator on a touch sensitive display screen, bypressing a button, or by providing another triggering input. In such anexample, trigger detector 672 can detect that speech processing system658 has been invoked when a triggering input via a user interfacemechanism is detected. Trigger detector 672 can detect that speechprocessing system 658 has been invoked in other ways as well.

Once speech processing system 658 is invoked, the speech input fromoperator 260 is provided to speech recognition component 674. Speechrecognition component 674 recognizes linguistic elements in the speechinput, such as words, phrases, or other linguistic units. Naturallanguage understanding system 678 identifies a meaning of the recognizedspeech. The meaning may be a natural language output, a command outputidentifying a command reflected in the recognized speech, a value outputidentifying a value in the recognized speech, or any of a wide varietyof other outputs that reflect the understanding of the recognizedspeech. For example, the natural language understanding system 678 andspeech processing system 568, more generally, may understand of themeaning of the recognized speech in the context of agriculturalharvester 100.

In some examples, speech processing system 658 can also generate outputsthat navigate operator 260 through a user experience based on the speechinput. For instance, dialog management system 680 may generate andmanage a dialog with the user in order to identify what the user wishesto do. The dialog may disambiguate a user's command; identify one ormore specific values that are needed to carry out the user's command; orobtain other information from the user or provide other information tothe user or both. Synthesis component 676 may generate speech synthesiswhich can be presented to the user through an audio operator interfacemechanism, such as a speaker. Thus, the dialog managed by dialogmanagement system 680 may be exclusively a spoken dialog or acombination of both a visual dialog and a spoken dialog.

Action signal generator 660 generates action signals to control operatorinterface mechanisms 218 based upon outputs from one or more of operatorinput command processing system 654, other controller interaction system656, and speech processing system 658. Visual control signal generator684 generates control signals to control visual items in operatorinterface mechanisms 218. The visual items may be lights, a displayscreen, warning indicators, or other visual items. Audio control signalgenerator 686 generates outputs that control audio elements of operatorinterface mechanisms 218. The audio elements include a speaker, audiblealert mechanisms, horns, or other audible elements. Haptic controlsignal generator 688 generates control signals that are output tocontrol haptic elements of operator interface mechanisms 218. The hapticelements include vibration elements that may be used to vibrate, forexample, the operator's seat, the steering wheel, pedals, or joysticksused by the operator. The haptic elements may include tactile feedbackor force feedback elements that provide tactile feedback or forcefeedback to the operator through operator interface mechanisms. Thehaptic elements may include a wide variety of other haptic elements aswell.

FIG. 12 is a flow diagram illustrating one example of the operation ofoperator interface controller 231 in generating an operator interfacedisplay on an operator interface mechanism 218, which can include atouch sensitive display screen. FIG. 12 also illustrates one example ofhow operator interface controller 231 can detect and process operatorinteractions with the touch sensitive display screen.

At block 692, operator interface controller 231 receives a map. Block694 indicates an example in which the map is a functional predictivemap, and block 696 indicates an example in which the map is another typeof map. At block 698, operator interface controller 231 receives aninput from geographic position sensor 204 identifying the geographiclocation of the agricultural harvester 100. As indicated in block 700,the input from geographic position sensor 204 can include the heading,along with the location, of agricultural harvester 100. Block 702indicates an example in which the input from geographic position sensor204 includes the speed of agricultural harvester 100, and block 704indicates an example in which the input from geographic position sensor204 includes other items.

At block 706, visual control signal generator 684 in operator interfacecontroller 231 controls the touch sensitive display screen in operatorinterface mechanisms 218 to generate a display showing all or a portionof a field represented by the received map. Block 708 indicates that thedisplayed field can include a current position marker showing a currentposition of the agricultural harvester 100 relative to the field. Block710 indicates an example in which the displayed field includes a nextwork unit marker that identifies a next work unit (or area on the field)in which agricultural harvester 100 will be operating. Block 712indicates an example in which the displayed field includes an upcomingarea display portion that displays areas that are yet to be processed byagricultural harvester 100, and block 714 indicates an example in whichthe displayed field includes previously visited display portions thatrepresent areas of the field that agricultural harvester 100 has alreadyprocessed. Block 716 indicates an example in which the displayed fielddisplays various characteristics of the field having georeferencedlocations on the map. For instance, if the received map is a yield map,the displayed field may show the different yield values in the fieldgeoreferenced within the displayed field. The mapped characteristics canbe shown in the previously visited areas (as shown in block 714), in theupcoming areas (as shown in block 712), and in the next work unit (asshown in block 710). Block 718 indicates an example in which thedisplayed field includes other items as well.

FIG. 13 is a pictorial illustration showing one example of a userinterface display 720 that can be generated on a touch sensitive displayscreen. In other instances, the user interface display 720 may begenerated on other types of displays. The touch sensitive display screenmay be mounted in the operator compartment of agricultural harvester 100or on the mobile device or elsewhere. User interface display 720 will bedescribed prior to continuing with the description of the flow diagramshown in FIG. 12.

In the example shown in FIG. 13, user interface display 720 illustratesthat the touch sensitive display screen includes a display feature foroperating a microphone 722 and a speaker 724. Thus, the touch sensitivedisplay may be communicably coupled to the microphone 722 and thespeaker 724. Block 726 indicates that the touch sensitive display screencan include a wide variety of user interface control actuators, such asbuttons, keypads, soft keypads, links, icons, switches, etc. Theoperator 260 can actuator the user interface control actuators toperform various functions.

In the example shown in FIG. 13, user interface display 720 includes afield display portion 728 that displays at least a portion of the fieldin which the agricultural harvester 100 is operating. The field displayportion 728 is shown with a current position marker 708 that correspondsto a current position of agricultural harvester 100 in the portion ofthe field shown in field display portion 728. In one example, theoperator may control the touch sensitive display in order to zoom intoportions of field display portion 728 or to pan or scroll the fielddisplay portion 728 to show different portions of the field. A next workunit 730 is shown as an area of the field directly in front of thecurrent position marker 708 of agricultural harvester 100. The currentposition marker 708 may also be configured to identify the direction oftravel of agricultural harvester 100, a speed of travel of agriculturalharvester 100 or both. In FIG. 13, the shape of the current positionmarker 708 provides an indication as to the orientation of theagricultural harvester 100 within the field which may be used as anindication of a direction of travel of the agricultural harvester 100.

The size of the next work unit 730 marked on field display portion 728may vary based upon a wide variety of different criteria. For instance,the size of next work unit 730 may vary based on the speed of travel ofagricultural harvester 100. Thus, when the agricultural harvester 100 istraveling faster, then the area of the next work unit 730 may be largerthan the area of next work unit 730 if agricultural harvester 100 istraveling more slowly. Field display portion 728 is also showndisplaying previously visited area 714 and upcoming areas 712.Previously visited areas 714 represent areas that are already harvestedwhile upcoming areas 712 represent areas that still need to beharvested. The field display portion 728 is also shown displayingdifferent characteristics of the field. In the example illustrated inFIG. 13, the map that is being displayed is a yield map. Therefore, aplurality of different yield markers are displayed on field displayportion 728. There are a set of yield display markers 732 shown in thealready visited areas 714. There are also a set of yield display markers734 shown in the upcoming areas 712, and there are a set of yielddisplay markers 736 shown in the next work unit 730. FIG. 13 shows thatthe yield display markers 732, 734, and 736 are made up of differentsymbols. Each of the symbols represents a yield amount. In the exampleshown in FIG. 3, the @ symbol represents high yield; the * symbolrepresents medium yield; and the # symbol represents low yield. Thus,the field display portion 728 shows different amounts of yield that arelocated at different areas within the field. As described earlier, thedisplay markers 732 may be made up of different symbols, and, asdescribed below, the symbols may be any display feature such asdifferent colors, shapes, patterns, intensities, text, icons, or otherdisplay features. In some instances, each location of the field may havea display marker associated therewith. Thus, in some instances, adisplay marker may be provided at each location of the field displayportion 728 to identify the nature of the characteristic being mappedfor each particular location of the field. Consequently, the presentdisclosure encompasses providing a display marker, such as the losslevel display marker 732 (as in the context of the present example ofFIG. 11), at one or more locations on the field display portion 728 toidentify the nature, degree, etc., of the characteristic beingdisplayed, thereby identifying the characteristic at the correspondinglocation in the field being displayed.

In the example of FIG. 13, user interface display 720 also has a controldisplay portion 738. Control display portion 738 allows the operator toview information and to interact with user interface display 720 invarious ways.

The actuators and display markers in portion 738 may be displayed as,for example, individual items, fixed lists, scrollable lists, drop downmenus, or drop down lists. In the example shown in FIG. 13, displayportion 738 shows information for the three different amounts of yieldthat correspond to the three symbols mentioned above. In other examples,yield values can be in greater granularity than the three levels ofyield values shown. Display portion 738 also includes a set of touchsensitive actuators with which the operator 260 can interact by touch.For example, the operator 260 may touch the touch sensitive actuatorswith a finger to activate the respective touch sensitive actuator.

A flag column 739 shows flags that have been automatically or manuallyset. Flag actuator 740 allows operator 260 to mark a location, and thenadd information indicating the values yield at the location. Forinstance, when the operator 260 actuates the flag actuator 740 bytouching the flag actuator 740, touch gesture handling system 664 inoperator interface controller 231 identifies the current location as onewhere the yield is high. Or for instance, when the operator 260 touchesthe button 742, touch gesture handling system 664 identifies the currentlocation as a location where medium yield is present. Or for instance,when the operator 260 touches the button 744, touch gesture handlingsystem 664 identifies the current location as a location where low yieldis present. Touch gesture handling system 664 also controls visualcontrol signal generator 684 to add a symbol corresponding to theidentified yield value on field display portion 728 at a location theuser identifies before or after or during actuation of buttons 740, 742or 744.

Column 746 displays the symbols corresponding to each yield value thatis being tracked on the field display portion 728. Designator column 748shows the designator (which may be a textual designator or otherdesignator) identifying the yield value. Without limitation, the yieldsymbols in column 746 and the designators in column 748 can include anydisplay feature such as different colors, shapes, patterns, intensities,text, icons, or other display feature. Column 750 shows measured yieldvalues. In the example shown in FIG. 13, the yield values are valuesrepresentative of bushels per acre of corn. The values displayed incolumn 750 can be predicted values or values measured by in-situ sensors208. In one example, the operator 260 can select the particular part offield display portion 728 for which the values in column 750 are to bedisplayed. Thus, the values in column 750 can correspond to values indisplay portions 712, 714 or 730. Column 752 displays action thresholdvalues. Action threshold values in column 752 may be threshold valuescorresponding to the measured values in column 750. If the measuredvalues in column 750 satisfy the corresponding action threshold valuesin column 752, then control system 214 takes the action identified incolumn 754. In some instances, a measured value may satisfy acorresponding action threshold value by meeting or exceeding thecorresponding action threshold value. In one example, operator 260 canselect a threshold value, for example, in order to change the thresholdvalue by touching the threshold value in column 752. Once selected, theoperator 260 may change the threshold value. The threshold values incolumn 752 can be configured such that the designated action isperformed when the measured value 750 exceeds the threshold value,equals the threshold value, or is less than the threshold value.

Similarly, operator 260 can touch the action identifiers in column 754to change the action that is to be taken. When a threshold is met,multiple actions may be taken. For instance, at the bottom of column754, an increase speed action 756 and an lower header action 758 areidentified as actions that will be taken if the measured value in column750 meets the threshold value in column 752.

The actions that can be set in column 754 can be any of a wide varietyof different types of actions. For example, the actions can include akeep out action which, when executed, inhibits agricultural harvester100 from further harvesting in an area. The actions can include a speedchange action which, when executed, changes the travel speed ofagricultural harvester 100 through the field. The actions can include asetting change action for changing a setting of an internal actuator oranother WMA or set of WMAs or for implementing a settings change actionthat changes a setting of a header. These are examples only, and a widevariety of other actions are contemplated herein.

The display markers shown on user interface display 720 can be visuallycontrolled. Visually controlling the interface display 720 may beperformed to capture the attention of operator 260. For instance, thedisplay markers can be controlled to modify the intensity, color, orpattern with which the display markers are displayed. Additionally, thedisplay markers may be controlled to flash. The described alterations tothe visual appearance of the display markers are provided as examples.Consequently, other aspects of the visual appearance of the displaymarkers may be altered. Therefore, the display markers can be modifiedunder various circumstances in a desired manner in order, for example,to capture the attention of operator 260.

Various functions that can be accomplished by the operator 260 usinguser interface display 720 can also be accomplished automatically, suchas by other controllers in control system 214.

Returning now to the flow diagram of FIG. 12, the description of theoperation of operator interface controller 231 continues. At block 760,operator interface controller 231 detects an input setting a flag andcontrols the touch sensitive user interface display 720 to display theflag on field display portion 728. The detected input may be an operatorinput, as indicated at 762, or an input from another controller, asindicated at 764. At block 766, operator interface controller 231detects an in-situ sensor input indicative of a measured characteristicof the field from one of the in-situ sensors 208. At block 768, visualcontrol signal generator 684 generates control signals to control userinterface display 720 to display actuators for modifying user interfacedisplay 720 and for modifying machine control. For instance, block 770represents that one or more of the actuators for setting or modifyingthe values in columns 739, 746, and 748 can be displayed. Thus, the usercan set flags and modify characteristics of those flags. For example, auser can modify the yield values or ranges, shown in column 750, anddesignators corresponding to the flags. Block 772 represents that actionthreshold values in column 752 are displayed. Block 776 represents thatthe actions in column 754 are displayed, and block 778 represents thatthe measured in-situ data in column 750 is displayed. Block 780indicates that a wide variety of other information and actuators can bedisplayed on user interface display 720 as well.

At block 782, operator input command processing system 654 detects andprocesses operator inputs corresponding to interactions with the userinterface display 720 performed by the operator 260. Where the userinterface mechanism on which user interface display 720 is displayed isa touch sensitive display screen, interaction inputs with the touchsensitive display screen by the operator 260 can be touch gestures 784.In some instances, the operator interaction inputs can be inputs using apoint and click device 786 or other operator interaction inputs 788.

At block 790, operator interface controller 231 receives signalsindicative of an alert condition. For instance, block 792 indicates thatsignals may be received by controller input processing system 668indicating that detected values in column 750 satisfy thresholdconditions present in column 752. As explained earlier, the thresholdconditions may include values being below a threshold, at a threshold,or above a threshold. Block 794 shows that action signal generator 660can, in response to receiving an alert condition, alert the operator 260by using visual control signal generator 684 to generate visual alerts,by using audio control signal generator 686 to generate audio alerts, byusing haptic control signal generator 688 to generate haptic alerts, orby using any combination of these. Similarly, as indicated by block 796,controller output generator 670 can generate outputs to othercontrollers in control system 214 so that those controllers perform thecorresponding action identified in column 754. Block 798 shows thatoperator interface controller 231 can detect and process alertconditions in other ways as well.

Block 900 shows that speech handling system 662 may detect and processinputs invoking speech processing system 658. Block 902 shows thatperforming speech processing may include the use of dialog managementsystem 680 to conduct a dialog with the operator 260. Block 904 showsthat the speech processing may include providing signals to controlleroutput generator 670 so that control operations are automaticallyperformed based upon the speech inputs.

Table 1, below, shows an example of a dialog between operator interfacecontroller 231 and operator 260. In Table 1, operator 260 uses a triggerword or a wakeup word that is detected by trigger detector 672 to invokespeech processing system 658. In the example shown in Table 1, thewakeup word is “Johnny”.

TABLE 1 Operator: “Johnny, tell me about current yield” OperatorInterface Controller: “Current yield is 95 bushels/per acre of corn”Operator: “Johnny, what should I do because of this yield?” OperatorInterface Controller: “Yield is low. Consider speeding up, grain losswill not be significantly affected.”

Table 2 shows an example in which speech synthesis component 676provides an output to audio control signal generator 686 to provideaudible updates on an intermittent or periodic basis. The intervalbetween updates may be time-based, such as every five minutes, orcoverage or distance-based, such as every five acres, orexception-based, such as when a measured value is greater than athreshold value.

TABLE 2 Operator Interface Controller: “Over last 10 minutes, biomassaveraged: 4 tons/acre.” Operator Interface Controller: “Next 1 acreestimated to average 7 tons/acre biomass.” Operator InterfaceController: “Warning: Biomass approaching maximum threshold.” OperatorInterface Controller: “Caution: Biomass exceeding maximum threshold.Mitigation options include: Slowing Machine and Raising Header.”Operator: “Johnny, Raise header” Operator Interface Controller: “RaisingHeader to reduce biomass 5% below maximum threshold.”

The example shown in Table 3 illustrates that some actuators or userinput mechanisms on the touch sensitive display 720 can be supplementedwith speech dialog. The example in Table 3 illustrates that actionsignal generator 660 can generate action signals to automatically mark ahigh yield area in the field being harvested.

TABLE 3   Human: “Johnny, mark high yield area.” Operator InterfaceController: “High yield area marked.”

The example shown in Table 4 illustrates that action signal generator660 can conduct a dialog with operator 260 to begin and end marking ofan area based on the yield value.

TABLE 4   Human: “Johnny, start marking high yield area.” OperatorInterface Controller: “Marking high yield area.” Human: “Johnny, stopmarking high yield area.” Operator Interface Controller: “High yieldarea marking stopped.”

The example shown in Table 5 illustrates that action signal generator160 can generate signals to mark an area based on the yield value in adifferent way than those shown in Tables 3 and 4.

TABLE 5 Human: “Johnny, mark next 100 feet as a low yield area.”Operator Interface Controller: “Next 100 feet marked as a low yieldarea.”

Returning again to FIG. 12, block 906 illustrates that operatorinterface controller 231 can detect and process conditions foroutputting a message or other information in other ways as well. Forinstance, other controller interaction system 656 can detect inputs fromother controllers indicating that alerts or output messages should bepresented to operator 260. Block 908 shows that the outputs can be audiomessages. Block 910 shows that the outputs can be visual messages, andblock 912 shows that the outputs can be haptic messages. Until operatorinterface controller 231 determines that the current harvestingoperation is completed, as indicated by block 914, processing reverts toblock 698 where the geographic location of harvester 100 is updated andprocessing proceeds as described above to update user interface display720.

Once the operation is complete, then any desired values that aredisplayed, or have been displayed on user interface display 720, can besaved. Those values can also be used in machine learning to improvedifferent portions of predictive model generator 210, predictive mapgenerator 212, control zone generator 213, control algorithms, or otheritems. Saving the desired values is indicated by block 916. The valuescan be saved locally on agricultural harvester 100, or the values can besaved at a remote server location or sent to another remote system.

It can thus be seen that prior information map is obtained by anagricultural harvester and shows vegetative index or yield values atdifferent geographic locations of a field being harvested. An in-situsensor on the harvester senses a characteristic that has valuesindicative of an agricultural characteristic as the agriculturalharvester moves through the field. A predictive map generator generatesa predictive map that predicts control values for different locations inthe field based on the values of the vegetative index or yield in theprior information map and the agricultural characteristic sensed by thein-situ sensor. A control system controls controllable subsystem basedon the control values in the predictive map.

A control value is a value upon which an action can be based. A controlvalue, as described herein, can include any value (or characteristicsindicated by or derived from the value) that may be used in the controlof agricultural harvester 100. A control value can be any valueindicative of an agricultural characteristic. A control value can be apredicted value, a measured value, or a detected value. A control valuemay include any of the values provided by a map, such as any of the mapsdescribed herein, for instance, a control value can be a value providedby an information map, a value provided by prior information map, or avalue provided predictive map, such as a functional predictive map. Acontrol value can also include any of the characteristics indicated byor derived from the values detected by any of the sensors describedherein. In other examples, a control value can be provided by anoperator of the agricultural machine, such as a command input by anoperator of the agricultural machine.

The present discussion has mentioned processors and servers. In someexamples, the processors and servers include computer processors withassociated memory and timing circuitry, not separately shown. Theprocessors and servers are functional parts of the systems or devices towhich the processors and servers belong and are activated by andfacilitate the functionality of the other components or items in thosesystems.

Also, a number of user interface displays have been discussed. Thedisplays can take a wide variety of different forms and can have a widevariety of different user actuatable operator interface mechanismsdisposed thereon. For instance, user actuatable operator interfacemechanisms may include text boxes, check boxes, icons, links, drop-downmenus, search boxes, etc. The user actuatable operator interfacemechanisms can also be actuated in a wide variety of different ways. Forinstance, the user actuatable operator interface mechanisms can beactuated using operator interface mechanisms such as a point and clickdevice, such as a track ball or mouse, hardware buttons, switches, ajoystick or keyboard, thumb switches or thumb pads, etc., a virtualkeyboard or other virtual actuators. In addition, where the screen onwhich the user actuatable operator interface mechanisms are displayed isa touch sensitive screen, the user actuatable operator interfacemechanisms can be actuated using touch gestures. Also, user actuatableoperator interface mechanisms can be actuated using speech commandsusing speech recognition functionality. Speech recognition may beimplemented using a speech detection device, such as a microphone, andsoftware that functions to recognize detected speech and executecommands based on the received speech.

A number of data stores have also been discussed. It will be noted thedata stores can each be broken into multiple data stores. In someexamples, one or more of the data stores may be local to the systemsaccessing the data stores, one or more of the data stores may all belocated remote form a system utilizing the data store, or one or moredata stores may be local while others are remote. All of theseconfigurations are contemplated by the present disclosure.

Also, the figures show a number of blocks with functionality ascribed toeach block. It will be noted that fewer blocks can be used to illustratethat the functionality ascribed to multiple different blocks isperformed by fewer components. Also, more blocks can be usedillustrating that the functionality may be distributed among morecomponents. In different examples, some functionality may be added, andsome may be removed.

It will be noted that the above discussion has described a variety ofdifferent systems, components, logic, and interactions. It will beappreciated that any or all of such systems, components, logic andinteractions may be implemented by hardware items, such as processors,memory, or other processing components, including but not limited toartificial intelligence components, such as neural networks, some ofwhich are described below, that perform the functions associated withthose systems, components, logic, or interactions. In addition, any orall of the systems, components, logic and interactions may beimplemented by software that is loaded into a memory and is subsequentlyexecuted by a processor or server or other computing component, asdescribed below. Any or all of the systems, components, logic andinteractions may also be implemented by different combinations ofhardware, software, firmware, etc., some examples of which are describedbelow. These are some examples of different structures that may be usedto implement any or all of the systems, components, logic andinteractions described above. Other structures may be used as well.

FIG. 14 is a block diagram of agricultural harvester 600, which may besimilar to agricultural harvester 100 shown in FIG. 2. The agriculturalharvester 600 communicates with elements in a remote server architecture500. In some examples, remote server architecture 500 providescomputation, software, data access, and storage services that do notrequire end-user knowledge of the physical location or configuration ofthe system that delivers the services. In various examples, remoteservers may deliver the services over a wide area network, such as theinternet, using appropriate protocols. For instance, remote servers maydeliver applications over a wide area network and may be accessiblethrough a web browser or any other computing component. Software orcomponents shown in FIG. 2 as well as data associated therewith, may bestored on servers at a remote location. The computing resources in aremote server environment may be consolidated at a remote data centerlocation, or the computing resources may be dispersed to a plurality ofremote data centers. Remote server infrastructures may deliver servicesthrough shared data centers, even though the services appear as a singlepoint of access for the user. Thus, the components and functionsdescribed herein may be provided from a remote server at a remotelocation using a remote server architecture. Alternatively, thecomponents and functions may be provided from a server, or thecomponents and functions can be installed on client devices directly, orin other ways.

In the example shown in FIG. 14, some items are similar to those shownin FIG. 2 and those items are similarly numbered. FIG. 14 specificallyshows that predictive model generator 210 or predictive map generator212, or both, may be located at a server location 502 that is remotefrom the agricultural harvester 600. Therefore, in the example shown inFIG. 14, agricultural harvester 600 accesses systems through remoteserver location 502.

FIG. 14 also depicts another example of a remote server architecture.FIG. 14 shows that some elements of FIG. 2 may be disposed at a remoteserver location 502 while others may be located elsewhere. By way ofexample, data store 202 may be disposed at a location separate fromlocation 502 and accessed via the remote server at location 502.Regardless of where the elements are located, the elements can beaccessed directly by agricultural harvester 600 through a network suchas a wide area network or a local area network; the elements can behosted at a remote site by a service; or the elements can be provided asa service or accessed by a connection service that resides in a remotelocation. Also, data may be stored in any location, and the stored datamay be accessed by, or forwarded to, operators, users, or systems. Forinstance, physical carriers may be used instead of, or in addition to,electromagnetic wave carriers. In some examples, where wirelesstelecommunication service coverage is poor or nonexistent, anothermachine, such as a fuel truck or other mobile machine or vehicle, mayhave an automated, semi-automated, or manual information collectionsystem. As the combine harvester 600 comes close to the machinecontaining the information collection system, such as a fuel truck priorto fueling, the information collection system collects the informationfrom the combine harvester 600 using any type of ad-hoc wirelessconnection. The collected information may then be forwarded to anothernetwork when the machine containing the received information reaches alocation where wireless telecommunication service coverage or otherwireless coverage—is available. For instance, a fuel truck may enter anarea having wireless communication coverage when traveling to a locationto fuel other machines or when at a main fuel storage location. All ofthese architectures are contemplated herein. Further, the informationmay be stored on the agricultural harvester 600 until the agriculturalharvester 600 enters an area having wireless communication coverage. Theagricultural harvester 600, itself, may send the information to anothernetwork.

It will also be noted that the elements of FIG. 2, or portions thereof,may be disposed on a wide variety of different devices. One or more ofthose devices may include an on-board computer, an electronic controlunit, a display unit, a server, a desktop computer, a laptop computer, atablet computer, or other mobile device, such as a palm top computer, acell phone, a smart phone, a multimedia player, a personal digitalassistant, etc.

In some examples, remote server architecture 500 may includecybersecurity measures. Without limitation, these measures may includeencryption of data on storage devices, encryption of data sent betweennetwork nodes, authentication of people or processes accessing data, aswell as the use of ledgers for recording metadata, data, data transfers,data accesses, and data transformations. In some examples, the ledgersmay be distributed and immutable (e.g., implemented as blockchain).

FIG. 15 is a simplified block diagram of one illustrative example of ahandheld or mobile computing device that can be used as a user's orclient's hand held device 16, in which the present system (or parts ofit) can be deployed. For instance, a mobile device can be deployed inthe operator compartment of agricultural harvester 100 for use ingenerating, processing, or displaying the maps discussed above. FIGS.16-17 are examples of handheld or mobile devices.

FIG. 15 provides a general block diagram of the components of a clientdevice 16 that can run some components shown in FIG. 2, that interactswith them, or both. In the device 16, a communications link 13 isprovided that allows the handheld device to communicate with othercomputing devices and under some examples provides a channel forreceiving information automatically, such as by scanning. Examples ofcommunications link 13 include allowing communication though one or morecommunication protocols, such as wireless services used to providecellular access to a network, as well as protocols that provide localwireless connections to networks.

In other examples, applications can be received on a removable SecureDigital (SD) card that is connected to an interface 15. Interface 15 andcommunication links 13 communicate with a processor 17 (which can alsoembody processors or servers from other FIGS.) along a bus 19 that isalso connected to memory 21 and input/output (I/O) components 23, aswell as clock 25 and location system 27.

I/O components 23, in one example, are provided to facilitate input andoutput operations. I/O components 23 for various examples of the device16 can include input components such as buttons, touch sensors, opticalsensors, microphones, touch screens, proximity sensors, accelerometers,orientation sensors and output components such as a display device, aspeaker, and or a printer port. Other I/O components 23 can be used aswell.

Clock 25 illustratively comprises a real time clock component thatoutputs a time and date. It can also, illustratively, provide timingfunctions for processor 17.

Location system 27 illustratively includes a component that outputs acurrent geographical location of device 16. This can include, forinstance, a global positioning system (GPS) receiver, a LORAN system, adead reckoning system, a cellular triangulation system, or otherpositioning system. Location system 27 can also include, for example,mapping software or navigation software that generates desired maps,navigation routes and other geographic functions.

Memory 21 stores operating system 29, network settings 31, applications33, application configuration settings 35, data store 37, communicationdrivers 39, and communication configuration settings 41. Memory 21 caninclude all types of tangible volatile and non-volatilecomputer-readable memory devices. Memory 21 may also include computerstorage media (described below). Memory 21 stores computer readableinstructions that, when executed by processor 17, cause the processor toperform computer-implemented steps or functions according to theinstructions. Processor 17 may be activated by other components tofacilitate their functionality as well.

FIG. 16 shows one example in which device 16 is a tablet computer 600.In FIG. 16, computer 601 is shown with user interface display screen602. Screen 602 can be a touch screen or a pen-enabled interface thatreceives inputs from a pen or stylus. Tablet computer 600 may also usean on-screen virtual keyboard. Of course, computer 601 might also beattached to a keyboard or other user input device through a suitableattachment mechanism, such as a wireless link or USB port, for instance.Computer 601 may also illustratively receive voice inputs as well.

FIG. 17 is similar to FIG. 16 except that the device is a smart phone71. Smart phone 71 has a touch sensitive display 73 that displays iconsor tiles or other user input mechanisms 75. Mechanisms 75 can be used bya user to run applications, make calls, perform data transferoperations, etc. In general, smart phone 71 is built on a mobileoperating system and offers more advanced computing capability andconnectivity than a feature phone.

Note that other forms of the devices 16 are possible.

FIG. 18 is one example of a computing environment in which elements ofFIG. 2 can be deployed. With reference to FIG. 18, an example system forimplementing some embodiments includes a computing device in the form ofa computer 810 programmed to operate as discussed above. Components ofcomputer 810 may include, but are not limited to, a processing unit 820(which can comprise processors or servers from previous FIGS.), a systemmemory 830, and a system bus 821 that couples various system componentsincluding the system memory to the processing unit 820. The system bus821 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. Memory and programs described with respectto FIG. 2 can be deployed in corresponding portions of FIG. 18.

Computer 810 typically includes a variety of computer readable media.Computer readable media may be any available media that can be accessedby computer 810 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media is different from, anddoes not include, a modulated data signal or carrier wave. Computerreadable media includes hardware storage media including both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 810. Communication media mayembody computer readable instructions, data structures, program modulesor other data in a transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal.

The system memory 830 includes computer storage media in the form ofvolatile and/or nonvolatile memory or both such as read only memory(ROM) 831 and random access memory (RAM) 832. A basic input/outputsystem 833 (BIOS), containing the basic routines that help to transferinformation between elements within computer 810, such as duringstart-up, is typically stored in ROM 831. RAM 832 typically containsdata or program modules or both that are immediately accessible toand/or presently being operated on by processing unit 820. By way ofexample, and not limitation, FIG. 18 illustrates operating system 834,application programs 835, other program modules 836, and program data837.

The computer 810 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 18 illustrates a hard disk drive 841 that reads from or writes tonon-removable, nonvolatile magnetic media, an optical disk drive 855,and nonvolatile optical disk 856. The hard disk drive 841 is typicallyconnected to the system bus 821 through a non-removable memory interfacesuch as interface 840, and optical disk drive 855 are typicallyconnected to the system bus 821 by a removable memory interface, such asinterface 850.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (e.g., ASICs),Application-specific Standard Products (e.g., ASSPs), System-on-a-chipsystems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 18, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 810. In FIG. 18, for example, hard disk drive 841 isillustrated as storing operating system 844, application programs 845,other program modules 846, and program data 847. Note that thesecomponents can either be the same as or different from operating system834, application programs 835, other program modules 836, and programdata 837.

A user may enter commands and information into the computer 810 throughinput devices such as a keyboard 862, a microphone 863, and a pointingdevice 861, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 820 through a user input interface 860 that is coupledto the system bus, but may be connected by other interface and busstructures. A visual display 891 or other type of display device is alsoconnected to the system bus 821 via an interface, such as a videointerface 890. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 897 and printer 896,which may be connected through an output peripheral interface 895.

The computer 810 is operated in a networked environment using logicalconnections (such as a controller area network—CAN, local areanetwork—LAN, or wide area network WAN) to one or more remote computers,such as a remote computer 880.

When used in a LAN networking environment, the computer 810 is connectedto the LAN 871 through a network interface or adapter 870. When used ina WAN networking environment, the computer 810 typically includes amodem 872 or other means for establishing communications over the WAN873, such as the Internet. In a networked environment, program modulesmay be stored in a remote memory storage device. FIG. 18 illustrates,for example, that remote application programs 885 can reside on remotecomputer 880.

It should also be noted that the different examples described herein canbe combined in different ways. That is, parts of one or more examplescan be combined with parts of one or more other examples. All of this iscontemplated herein.

Example 1 is an agricultural work machine comprising:

a communication system that receives an information map that includesvalues of a yield corresponding to different geographic locations in afield;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristiccorresponding to a geographic location;

a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive control values to thedifferent geographic locations in the field based on the values of theyield in the information map and based on the value of the agriculturalcharacteristic;

a controllable subsystem; and

a control system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 2 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator comprises:

a predictive yield map generator that generates a functional predictiveyield map that maps predictive agricultural yield values to thedifferent geographic locations in the field.

Example 3 is the agricultural work machine of any or all previousexamples, wherein the control system comprises:

a feed rate controller that generates a feed rate control signal basedon the detected geographic location and the functional predictive yieldmap and controls the controllable subsystem based on the feed ratecontrol signal to control a feed rate of material through theagricultural work machine.

Example 4 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator comprises:

a predictive biomass map generator that generates a functionalpredictive biomass map that maps predictive biomass values to thedifferent geographic locations in the field.

Example 5 is the agricultural work machine of any or all previousexamples, wherein the control system comprises:

a settings controller that generates a speed control signal based on thedetected geographic location and the functional predictive biomass mapand controls the controllable subsystem based on the speed controlsignal to control a speed of the agricultural work machine.

Example 6 is the agricultural work machine of any or all previousexamples, wherein the predictive map generator comprises:

a predictive operator command map generator that generates a functionalpredictive operator command map that maps predictive operator commandsto the different geographic locations in the field.

Example 7 is the agricultural work machine of any or all previousexamples, wherein the control system comprises:

a settings controller that generates an operator command control signalindicative of an operator command based on the detected geographiclocation and the functional predictive operator command map and controlsthe controllable subsystem based on the operator command control signalto execute the operator command.

Example 8 is the agricultural work machine of any or all previousexamples, wherein the information map comprises a historical yield mapthat maps historical yield values to the different geographic locationsin the field.

Example 9 is the agricultural work machine of any or all previousexamples, wherein the control system further comprises:

an operator interface controller that generates a user interface maprepresentation of the functional predictive agricultural map, the userinterface map representation comprising a field portion with one or moremarkers indicating the predictive control values at one or moregeographic locations on the field portion.

Example 10 is the agricultural work machine of any or all previousexamples, wherein the operator interface controller generates the userinterface map representation to include an interactive display portionthat displays a value display portion indicative of a selected value, aninteractive threshold display portion indicative of an action threshold,and an interactive action display portion indicative of a control actionto be taken when one of the predictive control values satisfies theaction threshold in relation to the selected value, the control systemgenerating the control signal to control the controllable subsystembased on the control action.

Example 11 is a computer implemented method of controlling anagricultural work machine comprising:

obtaining an information map that includes values of a yieldcorresponding to different geographic locations in a field;

detecting a geographic location of the agricultural work machine;

detecting, with an in-situ sensor, a value of an agriculturalcharacteristic corresponding to a geographic location;

generating a functional predictive agricultural map of the field thatmaps predictive control values to the different geographic locations inthe field based on the values of the yield in the information map andbased on the value of the agricultural characteristic; and

controlling a controllable subsystem based on the geographic position ofthe agricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 12 is the computer implemented method of any or all previousexamples, wherein generating a functional predictive map comprises:

generating a functional predictive biomass map that maps predictivebiomass of material to the different geographic locations in the field.

Example 13 is the computer implemented method of any or all previousexamples, wherein controlling a controllable subsystem comprises:

generating a feed rate control signal based on the detected geographiclocation and the functional predictive biomass map; and

controlling the controllable subsystem based on the feed rate controlsignal to control a feed rate of material through the agricultural workmachine.

Example 14 is the computer implemented method of any or all previousexamples, wherein generating a functional predictive map comprises:

generating a functional predictive machine speed map that mapspredictive machine speed values to the different geographic locations inthe field.

Example 15 is the computer implemented method of any or all previousexamples, wherein controlling a controllable subsystem comprises:

generating a speed control signal based on the detected geographiclocation and the functional predictive machine speed map; and

controlling the controllable subsystem based on the speed control signalto control a speed of the agricultural work machine.

Example 16 is the computer implemented method of any or all previousexamples, wherein generating a functional predictive map comprises:

generating a functional predictive operator command map that mapspredictive operator commands to the different geographic locations inthe field.

Example 17 is the computer implemented method of any or all previousexamples, wherein controlling the controllable subsystem comprises:

generating an operator command control signal indicative of an operatorcommand based on the detected geographic location and the functionalpredictive operator command map; and

controlling the controllable subsystem based on the operator commandcontrol signal to execute the operator command.

Example 18 is the computer implemented method of any or all previousexamples and further comprising:

generating a predictive agricultural model that models a relationshipbetween the yield and the agricultural characteristic based on a valueof the yield in the information map at the geographic location and avalue of the agricultural characteristic sensed by the in-situ sensor atthe geographic location, wherein generating the functional predictiveagricultural map comprises generating the functional predictiveagricultural map based on the values of the yield in the information mapand based on the predictive agricultural model.

Example 19 is an agricultural work machine comprising:

a communication system that receives a information map that includesvalues of yield corresponding to different geographic locations in afield;

a geographic position sensor that detects a geographic location of theagricultural work machine;

an in-situ sensor that detects a value of an agricultural characteristiccorresponding to a geographic location;

a predictive model generator that generates a predictive agriculturalmodel that models a relationship between the yield and the agriculturalcharacteristic based on a value of the yield in the information map atthe geographic location and a value of the agricultural characteristicsensed by the in-situ sensor at the geographic location;

a predictive map generator that generates a functional predictiveagricultural map of the field that maps predictive control values to thedifferent geographic locations in the field based on the values of theyield in the information map and based on the predictive agriculturalmodel;

a controllable subsystem; and

a control system that generates a control signal to control thecontrollable subsystem based on the geographic position of theagricultural work machine and based on the control values in thefunctional predictive agricultural map.

Example 20 is the agricultural work machine of any or all previousexamples, wherein the control system comprises at least one of:

a feed rate controller that generates a feed rate control signal basedon the detected geographic location and the functional predictiveagricultural map and controls the controllable subsystem based on thefeed rate control signal to control a feed rate of material through theagricultural work machine;

a settings controller that generates a speed control signal based on thedetected geographic location and the functional predictive agriculturalmap and controls the controllable subsystem based on the speed controlsignal to control a speed of the agricultural work machine; and

a settings controller that generates an operator command control signalindicative of an operator command based on the detected geographiclocation and the functional predictive agricultural map and controls thecontrollable subsystem based on the operator command control signal toexecute the operator command.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter defined in the appended claims is not necessarilylimited to the specific features or acts described above. Rather, thespecific features and acts described above are disclosed as exampleforms of the claims.

What is claimed is:
 1. An agricultural work machine comprising: a communication system that receives an information map that includes values of a yield corresponding to different geographic locations in a field; a geographic position sensor that detects a geographic location of the agricultural work machine; an in-situ sensor that detects a value of an agricultural characteristic corresponding to a geographic location; a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the yield in the information map and based on the value of the agricultural characteristic; a controllable subsystem; and a control system that generates a control signal to control the controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
 2. The agricultural work machine of claim 1, wherein the predictive map generator comprises: a predictive yield map generator that generates a functional predictive yield map that maps predictive agricultural yield values to the different geographic locations in the field.
 3. The agricultural work machine of claim 2, wherein the control system comprises: a feed rate controller that generates a feed rate control signal based on the detected geographic location and the functional predictive yield map and controls the controllable subsystem based on the feed rate control signal to control a feed rate of material through the agricultural work machine.
 4. The agricultural work machine of claim 1, wherein the predictive map generator comprises: a predictive biomass map generator that generates a functional predictive biomass map that maps predictive biomass values to the different geographic locations in the field.
 5. The agricultural work machine of claim 4, wherein the control system comprises: a settings controller that generates a speed control signal based on the detected geographic location and the functional predictive biomass map and controls the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine.
 6. The agricultural work machine of claim 1, wherein the predictive map generator comprises: a predictive operator command map generator that generates a functional predictive operator command map that maps predictive operator commands to the different geographic locations in the field.
 7. The agricultural work machine of claim 6, wherein the control system comprises: a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map and controls the controllable subsystem based on the operator command control signal to execute the operator command.
 8. The agricultural work machine of claim 1, wherein the information map comprises a historical yield map that maps historical yield values to the different geographic locations in the field.
 9. The agricultural work machine of claim 1, wherein the control system further comprises: an operator interface controller that generates a user interface map representation of the functional predictive agricultural map, the user interface map representation comprising a field portion with one or more markers indicating the predictive control values at one or more geographic locations on the field portion.
 10. The agricultural work machine of claim 9, wherein the operator interface controller generates the user interface map representation to include an interactive display portion that displays a value display portion indicative of a selected value, an interactive threshold display portion indicative of an action threshold, and an interactive action display portion indicative of a control action to be taken when one of the predictive control values satisfies the action threshold in relation to the selected value, the control system generating the control signal to control the controllable subsystem based on the control action.
 11. A computer implemented method of controlling an agricultural work machine comprising: obtaining an information map that includes values of a yield corresponding to different geographic locations in a field; detecting a geographic location of the agricultural work machine; detecting, with an in-situ sensor, a value of an agricultural characteristic corresponding to a geographic location; generating a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the yield in the information map and based on the value of the agricultural characteristic; and controlling a controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
 12. The computer implemented method of claim 11, wherein generating a functional predictive map comprises: generating a functional predictive biomass map that maps predictive biomass of material to the different geographic locations in the field.
 13. The computer implemented method of claim 12, wherein controlling a controllable subsystem comprises: generating a feed rate control signal based on the detected geographic location and the functional predictive biomass map; and controlling the controllable subsystem based on the feed rate control signal to control a feed rate of material through the agricultural work machine.
 14. The computer implemented method of claim 11, wherein generating a functional predictive map comprises: generating a functional predictive machine speed map that maps predictive machine speed values to the different geographic locations in the field.
 15. The computer implemented method of claim 14, wherein controlling a controllable subsystem comprises: generating a speed control signal based on the detected geographic location and the functional predictive machine speed map; and controlling the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine.
 16. The computer implemented method of claim 11, wherein generating a functional predictive map comprises: generating a functional predictive operator command map that maps predictive operator commands to the different geographic locations in the field.
 17. The computer implemented method of claim 16, wherein controlling the controllable subsystem comprises: generating an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive operator command map; and controlling the controllable subsystem based on the operator command control signal to execute the operator command.
 18. The computer implemented method of claim 11 and further comprising: generating a predictive agricultural model that models a relationship between the yield and the agricultural characteristic based on a value of the yield in the information map at the geographic location and a value of the agricultural characteristic sensed by the in-situ sensor at the geographic location, wherein generating the functional predictive agricultural map comprises generating the functional predictive agricultural map based on the values of the yield in the information map and based on the predictive agricultural model.
 19. An agricultural work machine comprising: a communication system that receives a information map that includes values of yield corresponding to different geographic locations in a field; a geographic position sensor that detects a geographic location of the agricultural work machine; an in-situ sensor that detects a value of an agricultural characteristic corresponding to a geographic location; a predictive model generator that generates a predictive agricultural model that models a relationship between the yield and the agricultural characteristic based on a value of the yield in the information map at the geographic location and a value of the agricultural characteristic sensed by the in-situ sensor at the geographic location; a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive control values to the different geographic locations in the field based on the values of the yield in the information map and based on the predictive agricultural model; a controllable subsystem; and a control system that generates a control signal to control the controllable subsystem based on the geographic position of the agricultural work machine and based on the control values in the functional predictive agricultural map.
 20. The agricultural work machine of claim 19, wherein the control system comprises at least one of: a feed rate controller that generates a feed rate control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the feed rate control signal to control a feed rate of material through the agricultural work machine; a settings controller that generates a speed control signal based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the speed control signal to control a speed of the agricultural work machine; and a settings controller that generates an operator command control signal indicative of an operator command based on the detected geographic location and the functional predictive agricultural map and controls the controllable subsystem based on the operator command control signal to execute the operator command. 